diff --git a/experiments/temporal-models/temporal-model-leaderboard/dvc.yaml b/experiments/temporal-models/temporal-model-leaderboard/dvc.yaml
index 4f01d5ac..980392e1 100644
--- a/experiments/temporal-models/temporal-model-leaderboard/dvc.yaml
+++ b/experiments/temporal-models/temporal-model-leaderboard/dvc.yaml
@@ -79,6 +79,26 @@ stages:
     outs:
       - data/07_model_output/bbox-tube-temporal-gru-convnext-finetune
 
+  # Best combination from the tube-multiscale-fusion ablation sweep
+  # (tubelet spatial x transformer temporal = the dinov2_multiscale default:
+  # top F1, perfect recall, cheapest — see that experiment's
+  # COMBINED_VARIATIONS.md).
+  evaluate_tube_multiscale_fusion_dinov2_multiscale:
+    cmd: >-
+      uv run python scripts/evaluate.py
+      --model-name tube-multiscale-fusion-dinov2-multiscale
+      --model-type tube-multiscale-fusion
+      --model-package data/01_raw/models/tube-multiscale-fusion-dinov2-multiscale.zip
+      --test-dir data/01_raw/sequential_test/test
+      --output-dir data/07_model_output/tube-multiscale-fusion-dinov2-multiscale
+    deps:
+      - scripts/evaluate.py
+      - src/temporal_model_leaderboard
+      - data/01_raw/sequential_test
+      - data/01_raw/models/tube-multiscale-fusion-dinov2-multiscale.zip
+    outs:
+      - data/07_model_output/tube-multiscale-fusion-dinov2-multiscale
+
   leaderboard:
     cmd: >-
       uv run python scripts/leaderboard.py
diff --git a/experiments/temporal-models/temporal-model-leaderboard/pyproject.toml b/experiments/temporal-models/temporal-model-leaderboard/pyproject.toml
index dd88a13f..2b047937 100644
--- a/experiments/temporal-models/temporal-model-leaderboard/pyproject.toml
+++ b/experiments/temporal-models/temporal-model-leaderboard/pyproject.toml
@@ -9,6 +9,7 @@ dependencies = [
     "mtb-change-detection",
     "pyro-detector-baseline",
     "bbox-tube-temporal",
+    "tube-multiscale-fusion",
     "tqdm>=4.67.3",
 ]
 
@@ -18,6 +19,7 @@ tracking-fsm-baseline = { path = "../tracking-fsm-baseline" }
 mtb-change-detection = { path = "../mtb-change-detection" }
 pyro-detector-baseline = { path = "../pyro-detector-baseline" }
 bbox-tube-temporal = { path = "../bbox-tube-temporal" }
+tube-multiscale-fusion = { path = "../tube-multiscale-fusion" }
 
 [dependency-groups]
 dev = [
diff --git a/experiments/temporal-models/temporal-model-leaderboard/src/temporal_model_leaderboard/registry.py b/experiments/temporal-models/temporal-model-leaderboard/src/temporal_model_leaderboard/registry.py
index 36e7123c..3375c18d 100644
--- a/experiments/temporal-models/temporal-model-leaderboard/src/temporal_model_leaderboard/registry.py
+++ b/experiments/temporal-models/temporal-model-leaderboard/src/temporal_model_leaderboard/registry.py
@@ -26,6 +26,10 @@
         "bbox_tube_temporal.model",
         "BboxTubeTemporalModel",
     ),
+    "tube-multiscale-fusion": (
+        "tube_multiscale_fusion.model",
+        "TubeMultiscaleFusionModel",
+    ),
 }
 
 
diff --git a/experiments/temporal-models/temporal-model-leaderboard/uv.lock b/experiments/temporal-models/temporal-model-leaderboard/uv.lock
index 2204a1ba..c1157cf4 100644
--- a/experiments/temporal-models/temporal-model-leaderboard/uv.lock
+++ b/experiments/temporal-models/temporal-model-leaderboard/uv.lock
@@ -1,5 +1,5 @@
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-revision = 1
+revision = 3
 requires-python = ">=3.11"
 resolution-markers = [
     "python_full_version >= '3.14' and sys_platform == 'win32'",
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diff --git a/experiments/temporal-models/tube-multiscale-fusion/.dvc/.gitignore b/experiments/temporal-models/tube-multiscale-fusion/.dvc/.gitignore
new file mode 100644
index 00000000..528f30c7
--- /dev/null
+++ b/experiments/temporal-models/tube-multiscale-fusion/.dvc/.gitignore
@@ -0,0 +1,3 @@
+/config.local
+/tmp
+/cache
diff --git a/experiments/temporal-models/tube-multiscale-fusion/.dvc/config b/experiments/temporal-models/tube-multiscale-fusion/.dvc/config
new file mode 100644
index 00000000..bc5f2378
--- /dev/null
+++ b/experiments/temporal-models/tube-multiscale-fusion/.dvc/config
@@ -0,0 +1,5 @@
+[core]
+    remote = s3remote
+    analytics = false
+['remote "s3remote"']
+    url = s3://pyro-vision-rd/dvc/experiments/tube-multiscale-fusion/
diff --git a/experiments/temporal-models/tube-multiscale-fusion/.dvcignore b/experiments/temporal-models/tube-multiscale-fusion/.dvcignore
new file mode 100644
index 00000000..51973055
--- /dev/null
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@@ -0,0 +1,3 @@
+# Add patterns of files dvc should ignore, which could improve
+# the performance. Learn more at
+# https://dvc.org/doc/user-guide/dvcignore
diff --git a/experiments/temporal-models/tube-multiscale-fusion/.gitattributes b/experiments/temporal-models/tube-multiscale-fusion/.gitattributes
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+*.ipynb filter=nbstripout
+*.ipynb diff=ipynb
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--- /dev/null
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@@ -0,0 +1,8 @@
+__pycache__/
+*.pyc
+.venv/
+.ipynb_checkpoints/
+.pytest_cache/
+.ruff_cache/
+logs/
+datasets_full/
diff --git a/experiments/temporal-models/tube-multiscale-fusion/.python-version b/experiments/temporal-models/tube-multiscale-fusion/.python-version
new file mode 100644
index 00000000..2c073331
--- /dev/null
+++ b/experiments/temporal-models/tube-multiscale-fusion/.python-version
@@ -0,0 +1 @@
+3.11
diff --git a/experiments/temporal-models/tube-multiscale-fusion/ABLATIONS.md b/experiments/temporal-models/tube-multiscale-fusion/ABLATIONS.md
new file mode 100644
index 00000000..d1a3886d
--- /dev/null
+++ b/experiments/temporal-models/tube-multiscale-fusion/ABLATIONS.md
@@ -0,0 +1,119 @@
+# Ablation study — tube-multiscale-fusion
+
+This study decomposes the full two-branch model by removing or simplifying one
+component at a time, to attribute the model's performance to its parts. All
+runs use the **same data, seed (42), augmentation, training schedule, and tube
+geometry** (2×2 grid of 112×112 cells × 4-frame windows at stride 2 → 28 tubes
+per sequence) as the default model; only the named component changes. Metrics
+are on the **val split (280 tubes: 135 smoke / 145 fp)**, single seed.
+
+## Variants
+
+| # | Variant | What changed vs. full model |
+|---|---------|------------------------------|
+| 0 | **Full** | Global DINOv2 sequence branch + local tube branch + cross-attention fusion (self-attn over tubes, global=query). |
+| 1 | **− temporal** (`no_temporal`) | **Global branch removed.** Local branch + fusion kept; the fusion's global query is replaced by a learnable token. |
+| 2 | **− spatial** (`no_spatial`) | **Local branch removed.** Global branch only → MLP head; no fusion. |
+| 3 | **fusion → weighted-mean** (`weighted_mean`) | Both branches kept; **cross-attention fusion replaced** by a learned weighted mean over tubes + a learned gate between the two branches (no attention). |
+
+Each variant is a clean, single-component change: the kept modules are
+byte-for-byte identical to the full model.
+
+## Results (val, 280 tubes)
+
+### Classification quality
+
+| Variant | F1 | Accuracy | Precision | Recall | PR-AUC | FP | FN |
+|---------|---:|---:|---:|---:|---:|---:|---:|
+| **Full** | **0.9783** | 0.9786 | 0.9574 | **1.0000** | 0.9936 | 6 | 0 |
+| fusion → weighted-mean | 0.9779 | 0.9786 | **0.9708** | 0.9852 | **0.9952** | **4** | 2 |
+| − spatial (global only) | 0.9708 | 0.9714 | 0.9568 | 0.9852 | 0.9901 | 6 | 2 |
+| − temporal (local only) | 0.8716 | 0.8643 | 0.8012 | 0.9556 | 0.9019 | 32 | 6 |
+
+### Operating point & compute
+
+| Variant | Recall@FPR=1% | @5% | @10% | Params | Trainable | GFLOPs/seq | Latency (ms/seq)¹ |
+|---------|---:|---:|---:|---:|---:|---:|---:|
+| **Full** | **0.874** | **1.000** | **1.000** | 36.4 M | 16.6 M | 226.5 | 18.9 |
+| fusion → weighted-mean | 0.837 | 0.993 | 1.000 | 29.3 M | 9.5 M | 226.3 | 24.1 |
+| − spatial (global only) | 0.770 | 0.993 | 0.993 | 25.3 M | 5.4 M | 196.1 | 20.6 |
+| − temporal (local only) | 0.193 | 0.563 | 0.748 | 11.3 M | 11.3 M | 30.5 | 6.2 |
+
+¹ Batch-1, single RTX 4090, one consistent measurement pass. Batch-1 latency is
+dominated by kernel-launch overhead and is *not* strictly proportional to FLOPs
+(hence `no_spatial` ≈ `full` despite fewer FLOPs); treat GFLOPs as the reliable
+compute metric.
+
+## Findings
+
+### 1. The temporal (global) module is by far the most important component.
+
+Removing it (`no_temporal`) is **catastrophic**: F1 collapses 0.978 → 0.872,
+precision 0.957 → 0.801 (false positives jump 6 → 32), and recall at a strict
+1 % FPR falls off a cliff, 0.874 → 0.193. Recall holds up best (1.000 → 0.956),
+so the local branch alone still *finds* smoke — it simply cannot *reject*
+slow-moving look-alikes (cloud, fog, haze) without the long-range sequence
+context. The global branch is the workhorse; everything else refines it.
+
+### 2. The spatial (local) module adds a modest but real gain — concentrated at the strict operating point.
+
+Global-only (`no_spatial`) is already strong (F1 0.971), so the local branch
+buys only ~0.7 F1 on average. But its value shows up where it matters for
+deployment: **recall @ 1 % FPR improves 0.770 → 0.874** and PR-AUC 0.990 →
+0.994 when the local branch is added back. The high-frequency turbulence cue
+from the tubes sharpens the model's confidence precisely in the
+low-false-alarm regime — consistent with the design rationale that local motion
+detail disambiguates the hardest cases.
+
+### 3. The cross-attention fusion is **not** clearly better than a simple weighted mean.
+
+Replacing the attention-based fusion with a learned weighted mean
+(`weighted_mean`) **matches the full model**: F1 0.9779 vs 0.9783 (a one-tube
+difference, within noise), with *higher* precision (0.971 vs 0.957, 4 FP vs 6)
+and *higher* PR-AUC (0.9952 vs 0.9936). It trails the full model only at the
+very strictest 1 % FPR point (0.837 vs 0.874).
+
+The takeaway: on this dataset the gain comes from **having both branches**, not
+from the attention mechanism in the fusion step. Since the fusion is a
+negligible fraction of compute (both variants are ~226 GFLOPs — the cost is
+the 16 DINOv2 passes), the cross-attention buys a small edge at the extreme
+operating point for ~7 M extra parameters. The weighted-mean fusion is a strong
+**simplification candidate** if that extreme-FPR margin is not required.
+
+### Summary
+
+```
+contribution to val F1 (full = 0.978):
+  temporal/global module : +0.106   (0.872 → 0.978)   ← dominant
+  spatial/local module   : +0.007   (0.971 → 0.978)   ← modest, helps low-FPR
+  cross-attn over w-mean  : +0.000   (0.978 → 0.978)   ← negligible on this val set
+```
+
+The architecture's accuracy is driven overwhelmingly by the **global temporal
+context**, with the **local tube branch** adding a small but deployment-relevant
+gain at low false-alarm rates. The **attention-based fusion** is the most
+expendable piece: a learned weighted mean is statistically indistinguishable
+here and even slightly more precise.
+
+## Caveats
+
+- **Single seed, small val set (280 tubes).** Differences under ~0.005 F1
+  (e.g. full vs. weighted-mean) are within run-to-run noise; the temporal and
+  spatial effects are large enough to be robust, but the fusion comparison
+  should be confirmed with multiple seeds before acting on it.
+- All variants share the default 2×2 tube geometry; interactions between the
+  fusion choice and finer/denser tube geometries were not explored.
+
+## Reproduce
+
+```bash
+# Full model
+uv run dvc repro train_dinov2_multiscale evaluate_dinov2_multiscale
+# Ablations
+uv run dvc repro train_ablation_no_temporal      evaluate_ablation_no_temporal
+uv run dvc repro train_ablation_no_spatial       evaluate_ablation_no_spatial
+uv run dvc repro train_ablation_weighted_mean    evaluate_ablation_weighted_mean
+```
+
+Per-variant metrics, confusion matrices, and PR/ROC curves are written to
+`data/08_reporting/<variant>/{train,val}/`.
diff --git a/experiments/temporal-models/tube-multiscale-fusion/COMBINED_VARIATIONS.md b/experiments/temporal-models/tube-multiscale-fusion/COMBINED_VARIATIONS.md
new file mode 100644
index 00000000..eab9ff7c
--- /dev/null
+++ b/experiments/temporal-models/tube-multiscale-fusion/COMBINED_VARIATIONS.md
@@ -0,0 +1,106 @@
+# Combined spatial × temporal variations — tube-multiscale-fusion
+
+The [spatial](SPATIAL_COMPARISON.md) and [temporal](TEMPORAL_COMPARISON.md)
+comparisons each varied one module *in isolation* (local-only / global-only).
+This study puts both modules back together and sweeps the **cartesian product**
+of the strongest candidates **inside the full two-branch model** (global DINOv2
+branch + local tube branch + cross-attention fusion + head):
+
+- **spatial** (local per-tube encoder): `tubelet transformer`, `ViViT`, `3D ResNet`
+- **temporal** (global aggregator): `transformer`, `LSTM`
+
+= 6 full models. Same data, seed (42), schedule, augmentation, and 2×2 tube
+geometry; only the two module kinds change. `tubelet × transformer` is the
+production default (= `dinov2_multiscale`). Metrics on the **val split
+(280 tubes)**, single seed.
+
+## Results (val, 280 tubes)
+
+| Spatial | Temporal | F1 | Acc | Prec | Recall | PR-AUC | FP | FN | Params | GFLOPs | ms/seq¹ | R@FPR=1% | @5% |
+|---------|----------|---:|---:|---:|---:|---:|---:|---:|---:|---:|---:|---:|---:|
+| **tubelet** | **transformer** (default) | **0.9783** | 0.9786 | 0.9574 | **1.0000** | 0.9936 | 6 | **0** | 36.4 M | 226.5 | 10.8 | 0.874 | **1.000** |
+| **resnet3d** | **LSTM** | 0.9779 | 0.9786 | **0.9708** | 0.9852 | **0.9952** | **4** | 2 | 63.4 M | 800.1 | 23.1 | 0.911 | 0.993 |
+| vivit | LSTM | 0.9708 | 0.9714 | 0.9568 | 0.9852 | 0.9911 | 6 | 2 | 37.6 M | 250.9 | 12.8 | **0.933** | 0.993 |
+| resnet3d | transformer | 0.9675 | 0.9679 | 0.9437 | 0.9926 | 0.9946 | 8 | 1 | 65.7 M | 800.2 | 22.9 | 0.867 | 0.993 |
+| vivit | transformer | 0.9675 | 0.9679 | 0.9437 | 0.9926 | 0.9931 | 8 | 1 | 40.0 M | 251.0 | 12.6 | 0.793 | 0.993 |
+| tubelet | LSTM | 0.9673 | 0.9679 | 0.9500 | 0.9852 | 0.9950 | 7 | 2 | 34.1 M | 226.4 | 10.9 | 0.874 | 0.985 |
+
+(sorted by F1) ¹ Batch-1, single RTX 4090.
+
+## Findings
+
+### 1. In the full model, the spatial × temporal choice barely moves the needle.
+
+All six combinations land within **F1 0.967–0.978 (~1.1 points)** — essentially
+a tie on this val set. This is the key result and it contrasts sharply with the
+isolated [spatial study](SPATIAL_COMPARISON.md), where the encoder choice spanned
+9 F1 points (0.85–0.94). The explanation is the [ablation](ABLATIONS.md) finding:
+once the strong **global DINOv2 branch** is present, it carries the prediction
+and the local branch contributes only ~0.7 F1. So swapping the local encoder
+(tubelet → ViViT → 3D ResNet) or the aggregator (transformer ↔ LSTM) only
+reshuffles that small residual — within noise. **The architecture is robust to
+these choices; none of the expensive upgrades is worth it in the full model.**
+
+### 2. The cheapest model is also (statistically) the best.
+
+`tubelet × transformer` — the current default and the **least expensive**
+(226 GFLOPs, 10.8 ms, 36 M params) — has the top F1 (0.978), perfect recall
+(0 missed smokes), and R@5%FPR = 1.000. Nothing beats it by more than noise, and
+everything else costs more.
+
+### 3. `resnet3d × LSTM` is the only combo that rivals it — at 3.5× the compute.
+
+It edges the default on **precision (0.971 vs 0.957, 4 FP vs 6)** and **PR-AUC
+(0.9952)**, i.e. slightly cleaner false-alarm behaviour. But it costs **800
+GFLOPs (3.5×) and 23 ms (2.1×)** for a difference well inside single-seed noise.
+Not a worthwhile trade unless false positives are extremely costly *and* the
+compute budget is large.
+
+### 4. Temporal aggregator: LSTM ≈ transformer; spatial encoder: pretraining helps precision, not F1.
+
+- LSTM vs transformer is a wash across spatial backbones (LSTM wins for
+  resnet3d/vivit, transformer wins for tubelet — all within noise), and the
+  aggregator is compute-negligible (LSTM is marginally smaller).
+- The pretrained `3D ResNet` spatial encoder shows up only as a small
+  precision/PR-AUC edge (best with LSTM), **not** as an F1 win — consistent with
+  the local branch being a minor contributor here. Its standalone dominance in
+  the [spatial study](SPATIAL_COMPARISON.md) does **not** transfer to the full
+  model, because the global branch already supplies most of the signal.
+
+### Recommendation
+
+**Keep the current default, `tubelet × transformer`.** It is the cheapest and is
+statistically the best on this val set (top F1, perfect recall). If a deployment
+is acutely false-alarm-sensitive and compute is not a constraint,
+`resnet3d × LSTM` offers marginally higher precision/PR-AUC at 3.5× the FLOPs —
+otherwise it is not justified. `vivit × LSTM` is a middle option (best
+recall@1%FPR, +10% FLOPs) but no better on F1.
+
+The broader lesson across all four studies: **the global temporal branch is the
+workhorse; the local spatial branch and the specific module architectures are
+second-order.** Spend complexity budget on the global branch, not on heavier
+local encoders.
+
+## Caveats
+
+- **Single seed, small val set (280).** Every F1 gap here is < 0.012 — within
+  run-to-run noise. The compute/precision differences are robust; the F1
+  ranking is not. A multi-seed sweep would be needed to call a winner beyond the
+  default.
+- `tubelet × transformer` is reused from the existing `dinov2_multiscale` run
+  (identical config). The two `resnet3d` combos trained at batch size 8 (others
+  16) for memory; this affects optimisation noise only, not the reported
+  FLOPs/latency.
+
+## Reproduce
+
+```bash
+# Default cell (tubelet x transformer):
+uv run dvc repro train_dinov2_multiscale evaluate_dinov2_multiscale
+# The other 5 combos:
+uv run dvc repro train_full_combo evaluate_full_combo
+```
+
+Per-combo metrics, confusion matrices, and PR/ROC curves are in
+`data/08_reporting/full_<spatial>_<temporal>/{train,val}/` (and
+`data/08_reporting/dinov2_multiscale/` for the default cell).
diff --git a/experiments/temporal-models/tube-multiscale-fusion/Makefile b/experiments/temporal-models/tube-multiscale-fusion/Makefile
new file mode 100644
index 00000000..9953b376
--- /dev/null
+++ b/experiments/temporal-models/tube-multiscale-fusion/Makefile
@@ -0,0 +1,20 @@
+.PHONY: install lint format test help notebook
+
+help: ## Show this help
+	@grep -E '^[a-zA-Z_-]+:.*?## .*$$' $(MAKEFILE_LIST) | sort | awk 'BEGIN {FS = ":.*?## "}; {printf "\033[36m%-15s\033[0m %s\n", $$1, $$2}'
+
+install: ## Install dependencies and set up notebook output stripping
+	uv sync
+	uv run nbstripout --install
+
+lint: ## Run ruff linter on code and notebooks
+	uv run ruff check .
+
+format: ## Format code and notebooks with ruff
+	uv run ruff format .
+
+test: ## Run tests with pytest
+	uv run pytest tests/ -v
+
+notebook: ## Launch JupyterLab for notebooks
+	uv run jupyter lab notebooks/
diff --git a/experiments/temporal-models/tube-multiscale-fusion/README.md b/experiments/temporal-models/tube-multiscale-fusion/README.md
new file mode 100644
index 00000000..0ec60573
--- /dev/null
+++ b/experiments/temporal-models/tube-multiscale-fusion/README.md
@@ -0,0 +1,327 @@
+# tube-multiscale-fusion
+
+Two-branch temporal smoke classifier with multiscale spatio-temporal context.
+Evolution of [`bbox-tube-motion-fusion`](../bbox-tube-motion-fusion/): drops the
+per-frame motion CNN and adds a local spatio-temporal tube transformer that
+captures high-frequency detail, while keeping a global DINOv2 sequence context
+branch for long-range plume evolution.
+
+## Motivation
+
+Smoke and its hardest distractors — clouds, fog, haze, dust — can look almost
+identical in any *single* frame. What separates them is **how they move**, and
+crucially they move differently at different scales. This model is built around
+that observation, which is why it splits the problem into a **global** and a
+**local** branch instead of relying on one monolithic spatio-temporal encoder.
+
+### Why two branches instead of one
+
+A single encoder forced to describe a whole tube must trade off scales: pool
+aggressively and you keep the long-range shape but blur out fine texture; keep
+the texture and you blow up the token count and lose a clean notion of "the
+overall trajectory." Smoke discrimination needs *both* signals at once, and
+they live at genuinely different scales, so we model them with two specialised
+branches and let a fusion step decide how to combine them.
+
+- **Global branch — coarse shape over long time.** Each frame is embedded with
+  DINOv2 and the 16-frame sequence is summarised into one context vector. This
+  captures the *low-frequency* dynamics: is the region roughly static or slowly
+  drifting (clouds, fog banks, a stationary haze layer), or is it growing,
+  rising, and changing silhouette over many seconds (a developing plume)? This
+  is the question best answered by looking at the entire sequence at once, and
+  it is exactly the kind of smooth, large-scale evolution that survives heavy
+  spatial pooling.
+
+- **Local branch — high-frequency detail over short time.** The telltale sign
+  of *smoke* rather than a cloud is **turbulence**: the boundary flickers,
+  wisps detach, internal texture roils and reorganises from frame to frame.
+  This is a *high-frequency* spatio-temporal signal, and it is precisely what a
+  single global vector destroys — averaging the whole patch to 384 numbers
+  smooths away the very flicker that distinguishes turbulent smoke from the
+  laminar, low-frequency drift of fog or cloud. To preserve it we keep the
+  patch decomposed into small spatial cells and track each cell across a short
+  window of frames (a *tube*). Encoding each tube separately lets the model
+  read the local motion signature — turbulent and high-variance for smoke,
+  smooth and coherent for clouds/fog — that is invisible at the global scale.
+
+### Why process *tubes* (local motion) at all
+
+A static crop tells you what a region looks like; a tube tells you how that
+region *changes*. By feeding the local encoder short, overlapping spatial-
+temporal volumes we ask it to characterise per-cell motion rather than per-cell
+appearance. Turbulent smoke produces strong, irregular frame-to-frame change
+inside a cell; clouds and fog produce weak, slowly-varying change. Overlapping
+the temporal windows (stride < tube length) means no transition between frames
+falls on a window boundary, so short-lived turbulent events are always captured
+inside at least one tube. This is the high-frequency cue that appearance-only
+and globally-pooled models systematically miss, and it is why early, ambiguous
+frames — where a nascent plume still *looks* like haze — can still be flagged
+from their motion.
+
+### Why fuse with global-as-query cross-attention
+
+The two signals are complementary but asymmetric: the local tubes are the
+*evidence* (where and how strongly is something moving turbulently?), and the
+global vector is the *context* (over the whole sequence, is this consistent
+with a growing plume or with static weather?). We therefore let the local tube
+tokens first exchange information among themselves (self-attention, so a
+turbulent cell can be read relative to its neighbours), then let the **global
+vector act as the query** that attends over the local tubes (cross-attention).
+This phrases the final decision as: *"given the overall trajectory I observed,
+which local motion signatures matter, and do they add up to smoke?"* — using
+long-range context to weight and interpret the high-frequency local evidence,
+rather than averaging the two and hoping the signal survives.
+
+The empirical payoff (see Results) is that this RGB-only design matches the
+previous motion-CNN baseline **without** any precomputed optical-flow / frame-
+difference inputs: the local tube transformer learns the turbulence cue
+directly from pixels.
+
+## Approach
+
+```
+patches (B, 16, 3, 224, 224) + mask (B, 16)
+        │
+        ├──► Global branch
+        │       DINOv2 ViT-S/14 per frame  →  (B, 16, 384)
+        │       Transformer aggregator (CLS + pos)  →  (B, 384)  =  global_vec
+        │
+        └──► Local branch
+                extract_tubes:
+                    spatial unfold (grid_size × grid_size cells)
+                    temporal sliding windows (tube_length, temporal_stride)
+                    →  (B, N_tubes, 3, T_t, h, w)
+                LocalTubeEncoder per tube:
+                    tubelet Conv3d (t_kernel × h_patch × w_patch)
+                    → tokens + CLS → self-attention
+                    →  (B, N_tubes, 384)
+                tube_validity_mask  →  (B, N_tubes)
+                                 │
+                                 ▼
+        FusionModule (×2 blocks):
+            self-attn over local tubes
+            cross-attn: global=Q, locals=KV
+            →  (B, 384)
+                                 │
+                                 ▼
+        MLP head (Linear→GELU→Linear)  →  logit  (B,)
+```
+
+### Component summary
+
+| Module | File | Role |
+|--------|------|------|
+| `GlobalBranch` | `src/.../global_branch.py` | Frozen DINOv2 ViT-S/14 (last block finetuned) + 2-layer Transformer aggregator. |
+| `LocalBranch` | `src/.../local_branch.py` | `extract_tubes` + tubelet Conv3d + self-attn video transformer per tube. |
+| `FusionModule` | `src/.../fusion.py` | Self-attn over local tokens + cross-attn (global=Q, locals=KV). |
+| `TubeMultiscaleClassifier` | `src/.../classifier.py` | Glues the three modules + MLP classification head. |
+| `LitTubeMultiscaleClassifier` | `src/.../lit_module.py` | Lightning wrapper: BCE, AdamW two-group, cosine warmup, F1 callbacks. |
+
+### Default geometry
+
+Picked from the resolution sweep (see Results): a **2×2 spatial grid of
+112×112 cells** with **4-frame temporal windows at stride 2** — yielding
+7 × 4 = **28 tubes per sequence**.
+
+All knobs are exposed in `params.yaml` under `local_branch.*`.
+
+## Data
+
+Reuses the upstream pipeline from `bbox-tube-motion-fusion`:
+
+- `pyro-dataset v2.2.0` (sequential train/val) imported via DVC, truncated to
+  16 frames per sequence.
+- Greedy IoU tube matching + 1.5× context bbox crops, resized to 224×224
+  PNGs at `data/05_model_input/{train,val}/`.
+
+```bash
+uv sync
+uv run dvc import https://github.com/pyronear/pyro-dataset \
+    data/processed/sequential_train_val/train \
+    -o data/01_raw/datasets_full/train --rev v2.2.0
+uv run dvc import https://github.com/pyronear/pyro-dataset \
+    data/processed/sequential_train_val/val \
+    -o data/01_raw/datasets_full/val --rev v2.2.0
+uv run dvc pull
+```
+
+Train / val tube counts after IoU-based filtering: 2 831 / 280.
+
+## Results
+
+Resolution sweep (single-seed, same training schedule and 5-epoch early-stop
+patience on `val/f1`):
+
+| variant            | grid / tube_len / stride | tubes/seq | train F1 | val F1 | val acc | val prec | val rec | val PR-AUC |
+|--------------------|--------------------------|----------:|---------:|-------:|--------:|---------:|--------:|-----------:|
+| **default** (2×2)  | 2×2 / 4 / 2              |        28 |   0.9810 | **0.9783** | 0.9786 | 0.9574 | **1.0000** | 0.9936 |
+| spatial_4x4        | 4×4 / 4 / 2              |       112 |   0.9344 | 0.9673 |  0.9679 |   0.9500 |  0.9852 |     0.9874 |
+| spatial_8x8        | 8×8 / 4 / 2              |       448 |   0.9701 | 0.9745 |  0.9750 |   0.9571 |  0.9926 |     0.9942 |
+| temporal_stride1   | 2×2 / 4 / 1              |        52 |   0.9769 | 0.9781 |  0.9786 |   0.9640 |  0.9926 | **0.9954** |
+| temporal_len8      | 2×2 / 8 / 4              |        12 |   0.9760 | 0.9744 |  0.9750 |   0.9638 |  0.9852 |     0.9939 |
+
+> Note: sweep variants `temporal_stride1` and `temporal_len8` use the new
+> 2×2 spatial default; their tube counts are listed accordingly.
+
+**Confusion matrix (val, 280 sequences)** of the default 2×2 variant:
+
+| | predicted fp | predicted smoke |
+|---:|---:|---:|
+| **fp**    | 139 (TN) |   6 (FP) |
+| **smoke** |   0 (FN) | 135 (TP) |
+
+Compared to the **bbox-tube-motion-fusion** baseline:
+
+| model | val F1 | val acc | val prec | val rec | val PR-AUC | extra inputs |
+|---|---:|---:|---:|---:|---:|---|
+| bbox-tube-motion-fusion (`vit_dinov2_motion_frame_diff`) | 0.9776 | — | — | — | — | precomputed motion images |
+| **tube-multiscale-fusion (default 2×2)** | **0.9783** | 0.9786 | 0.9574 | **1.0000** | 0.9936 | RGB only |
+
+Same backbone (DINOv2 ViT-S/14, last block fine-tuned), same training schedule
+class, with **no motion-feature precomputation** — the local tube transformer
+learns the equivalent signal from raw pixels.
+
+### Benchmarking
+
+Standardized Pyronear R&D metrics (see [GUIDELINES.md](../../GUIDELINES.md))
+for the default 2×2 model on the val split (280 tubes), computed by
+`scripts/benchmark.py`. Hardware: NVIDIA RTX 4090 (GPU) / 24-thread CPU.
+
+| Metric | Value |
+|--------|-------|
+| **Recall @ FPR=1%** | 0.874 |
+| **Recall @ FPR=5%** | 1.000 |
+| **Recall @ FPR=10%** | 1.000 |
+| **Time-to-detection** (median) | 2 frames ≈ 30 s after tube start |
+| **Time-to-detection** (mean) | 2.07 frames ≈ 32 s; 135/135 positives eventually fire |
+| **Inference latency (GPU)** | 10.7 ms/sequence → 0.67 ms/frame |
+| **Inference latency (CPU)** | 290 ms/sequence → 18.2 ms/frame |
+| **Model size** | 36.4 M params (16.6 M trainable, 19.9 M frozen DINOv2) |
+| **FLOPs** | 226.5 GFLOPs/sequence → 14.2 GFLOPs/frame |
+
+Notes:
+
+- **Recall @ FPR** is the tube-level ROC operating point: at a 5 % false-alarm
+  rate every smoke tube in the val set is recovered. The 1 % point (0.874) is
+  the conservative-threshold regime relevant for low-nuisance deployment.
+- **Time-to-detection** is measured by feeding the model increasing frame
+  prefixes (k = 2…16, later frames masked — the same variable-length contract
+  used in training) and recording the first prefix whose probability crosses
+  0.5. Reported as `(k_fire − 1) × Δt`, with Δt = 30 s the median inter-frame
+  interval in the val sequences. Median detection at the 2nd frame means smoke
+  is typically flagged within one frame interval of the tube's first detection.
+- **FLOPs** is dominated by the 16 per-frame DINOv2 ViT-S/14 forward passes;
+  the local tube transformer and fusion add a small fraction on top.
+
+Reproduce with:
+
+```bash
+uv run dvc repro benchmark_dinov2_multiscale
+```
+
+### Ablation: remove the temporal module (global branch)
+
+To isolate what the **global DINOv2 sequence branch** (the "temporal module")
+contributes, we train a faithful ablation: the **local branch and the fusion
+module are the exact same modules with the same hyperparameters** as the full
+model, and the *only* change is that the global branch is removed. Because the
+fusion's cross-attention needs a query (the global context vector in the full
+model), that single input is replaced by a **learnable query token** — a
+constant, data-independent parameter. Everything else (local tube branch,
+fusion self-attention + cross-attention + FFN, MLP head) is byte-for-byte
+identical. Same tube geometry (2×2 / len 4 / stride 2 → 28 tubes/seq), schedule,
+and data.
+
+Removing the global branch removes the 16 per-frame DINOv2 forward passes that
+dominate the full model's compute, so the ablation is strictly *smaller and
+cheaper* — exactly the point of an ablation.
+
+| | **Full model** (global + local + fusion) | **Ablation** (no temporal module) |
+|---|---:|---:|
+| val F1 | **0.9783** | 0.8716 |
+| val accuracy | **0.9786** | 0.8643 |
+| val precision | **0.9574** | 0.8012 |
+| val recall | **1.0000** | 0.9556 |
+| val PR-AUC | **0.9936** | 0.9019 |
+| val FP / FN (of 280) | **6 / 0** | 32 / 6 |
+| Recall @ FPR=1% | **0.874** | 0.193 |
+| Recall @ FPR=5% | **1.000** | 0.563 |
+| Recall @ FPR=10% | **1.000** | 0.748 |
+| Params (trainable) | 36.4 M (16.6 M) | **11.3 M** (11.3 M) |
+| GFLOPs / sequence | 226.5 | **30.5** |
+| GPU latency (ms/seq) | 10.7 | **2.1** |
+
+**Takeaway.** Removing the temporal module costs ~11 F1 points, and the damage
+is concentrated exactly where the design predicts: **precision and the
+low-false-alarm regime**, not recall. False positives jump 5× (6 → 32),
+precision drops 0.957 → 0.801, and recall at a strict 1 % FPR collapses
+0.874 → 0.193 — while recall stays comparatively high (1.000 → 0.956). In other
+words, the local tube branch alone still *finds* smoke, but without the global
+context it can no longer reliably *reject* slow-moving look-alikes (cloud, fog,
+haze): local turbulence alone is an ambiguous cue, and it takes the long-range
+sequence context to confirm whether that motion is a growing plume or benign
+weather. The ablation is ~7× cheaper (30.5 vs 226.5 GFLOPs) precisely because it
+drops the DINOv2 sequence branch — but that branch is what buys the
+deployment-critical low-FPR operating point.
+
+Reproduce with:
+
+```bash
+uv run dvc repro train_ablation_no_temporal evaluate_ablation_no_temporal
+```
+
+## How to Reproduce
+
+```bash
+uv sync
+# Import data (see "Data" above)
+uv run dvc repro
+```
+
+Run just the default training and packaging:
+
+```bash
+uv run dvc repro train_dinov2_multiscale
+uv run dvc repro evaluate_dinov2_multiscale
+uv run dvc repro package_dinov2_multiscale
+```
+
+Re-run only the resolution sweep:
+
+```bash
+uv run dvc repro train_dinov2_multiscale_sweep evaluate_dinov2_multiscale_sweep
+```
+
+## Packaged model
+
+`scripts/package_model.py` produces a self-contained
+`data/07_model_output/dinov2_multiscale/model_package.zip` (~245 MB) with:
+
+- `checkpoint.pt` — Lightning checkpoint
+- `params.yaml` — exactly the `global_branch`, `local_branch`, `fusion`, and
+  training-variant blocks used to train this checkpoint
+- `manifest.json` — package version, SHA-256, params key
+- `README.md` — loading instructions and inference contract
+
+To load the packaged model:
+
+```python
+import zipfile, tempfile
+from pathlib import Path
+from tube_multiscale_fusion.lit_module import LitTubeMultiscaleClassifier
+
+with zipfile.ZipFile("model_package.zip") as zf, tempfile.TemporaryDirectory() as td:
+    zf.extractall(td)
+    lit = LitTubeMultiscaleClassifier.load_from_checkpoint(
+        str(Path(td) / "checkpoint.pt"), pretrained=False
+    )
+    lit.eval()
+```
+
+## Tests
+
+```bash
+make install
+make test                            # all tests
+uv run pytest tests/ -m "not slow"   # skip the heavy DINOv2 download tests
+```
diff --git a/experiments/temporal-models/tube-multiscale-fusion/SPATIAL_COMPARISON.md b/experiments/temporal-models/tube-multiscale-fusion/SPATIAL_COMPARISON.md
new file mode 100644
index 00000000..5c545b3e
--- /dev/null
+++ b/experiments/temporal-models/tube-multiscale-fusion/SPATIAL_COMPARISON.md
@@ -0,0 +1,117 @@
+# Spatial-module comparison — tube-multiscale-fusion
+
+This compares architectures for the **spatial module**: the per-tube encoder in
+the local branch that turns each spatio-temporal tube `(3, T, h, w)` into one
+vector. It is the local-branch counterpart of [TEMPORAL_COMPARISON.md](TEMPORAL_COMPARISON.md).
+
+## Setup
+
+To isolate the spatial module, all runs use the **local-only** setting
+(`ablation_variant: no_temporal`): the global DINOv2 sequence branch is removed,
+tubes are extracted (2×2 grid of 112×112 cells × 4-frame windows at stride 2 →
+28 tubes/seq), each tube is embedded by the **encoder under test**, the tube
+vectors go through the fusion module (self-attention + learnable-query
+cross-attention) and an MLP head. Only the per-tube encoder varies. Same data,
+seed (42), schedule, augmentation. Metrics on the **val split (280 tubes)**,
+single seed.
+
+> Because the global branch is removed, these are *local-only* numbers — well
+> below the full two-branch model (F1 0.978). This study answers "which encoder
+> is the best **spatial module**", not "which model is best overall". See the
+> [ablation study](ABLATIONS.md) for the branch-level picture.
+
+| Encoder | Description |
+|---------|-------------|
+| **Tubelet transformer** (baseline/current) | Conv3d tubelet embedding → `[CLS]` + joint spatio-temporal self-attention. |
+| **3D ResNet** | Kinetics-400 **pretrained** `r3d_18` per tube. |
+| **ViViT** | Conv3d tubelet embedding → **factorised** spatial-then-temporal self-attention (ViViT Model-3). |
+| **ConvLSTM** | Per-frame 2D CNN stem → ConvLSTM recurrence over the tube's frames. |
+| **TSM** | Temporal Shift Module: 2D CNN with temporal channel shifts → temporal mean pool. |
+
+All encoders except 3D ResNet are trained from scratch.
+
+## Results (val, 280 tubes)
+
+| Encoder | F1 | Acc | Prec | Recall | PR-AUC | FP | FN | Params | GFLOPs/seq | ms/seq¹ | R@FPR=1% | @5% |
+|---------|---:|---:|---:|---:|---:|---:|---:|---:|---:|---:|---:|---:|
+| **3D ResNet** (Kinetics) | **0.9416** | **0.9429** | **0.9281** | 0.9556 | **0.9788** | **10** | 6 | 40.6 M | 604.2 | 14.1 | **0.526** | **0.941** |
+| **ViViT** (factorised) | 0.8904 | 0.8857 | 0.8280 | 0.9630 | 0.9235 | 27 | 5 | 14.8 M | 54.9 | 4.0 | 0.119 | 0.704 |
+| **Tubelet transformer** (baseline) | 0.8716 | 0.8643 | 0.8012 | 0.9556 | 0.9019 | 32 | 6 | 11.3 M | 30.5 | 2.1 | 0.193 | 0.563 |
+| **ConvLSTM** | 0.8581 | 0.8429 | 0.7600 | **0.9852** | 0.8679 | 42 | **2** | 8.7 M | 72.7 | 3.5 | 0.215 | 0.370 |
+| **TSM** | 0.8503 | 0.8429 | 0.7862 | 0.9259 | 0.8594 | 34 | 10 | 7.7 M | 27.4 | 2.8 | 0.148 | 0.444 |
+
+(sorted by F1) ¹ Batch-1, single RTX 4090.
+
+## Findings
+
+### 1. The spatial encoder choice matters a *lot* — far more than the temporal one.
+
+F1 spans **0.85 → 0.94 (≈9 points)** across encoders, versus the ~0.8-point
+spread in the [temporal comparison](TEMPORAL_COMPARISON.md). The reason is
+structural: in the local-only setting the per-tube encoder *is* the entire
+feature extractor (there is no DINOv2 global branch to lean on), so its quality
+dominates the result. This is the opposite regime from the temporal study,
+where strong DINOv2 features made the aggregator a minor lever.
+
+### 2. Pretraining is the dominant factor — 3D ResNet wins decisively.
+
+`r3d_18` is the **only** pretrained encoder and it is far ahead: **F1 0.942,
+PR-AUC 0.979, recall@5%FPR 0.941**, and it roughly **thirds the false positives**
+(10 vs the baseline's 32). Kinetics-400 ships it with strong, general motion
+priors that the from-scratch encoders cannot learn from ~2.8 k training tubes.
+The cost is steep: **20× the FLOPs** (604 vs 30.5) and the most parameters
+(40.6 M), since `r3d_18` runs over all 28 tubes per sequence.
+
+### 3. Among from-scratch encoders, factorised attention (ViViT) is best.
+
+Excluding the pretrained model, the ranking is
+**ViViT (0.890) > tubelet-transformer (0.872) > ConvLSTM (0.858) > TSM (0.850)**.
+ViViT's factorised spatial-then-temporal attention edges the current joint
+self-attention tubelet encoder by ~1.9 F1 at ~2× the FLOPs (54.9 vs 30.5) —
+a modest, cheap upgrade.
+
+### 4. ConvLSTM and TSM are not worth it here.
+
+Both are the lightest (7–9 M params) but the **worst on F1 and precision**: they
+over-fire (ConvLSTM recall 0.985 but precision 0.76, 42 FP). Recurrent/shift
+convolutional encoders trained from scratch on this small dataset don't match
+even the from-scratch transformers, and bring no compute advantage over the
+baseline tubelet encoder.
+
+### Recommendation
+
+- **In isolation, `r3d_18` is the clear best spatial module** — pretraining is
+  the decisive factor. Choose it if the local branch is to be a primary
+  pathway, and budget for its 20× FLOPs.
+- **For the actual two-branch model**, recall from the [ablation study](ABLATIONS.md)
+  that the local branch adds only ~0.7 F1 on top of the global branch. So the
+  expensive `r3d_18` is likely a poor trade there, and **ViViT** is the more
+  sensible upgrade over the current tubelet-transformer: slightly better, still
+  cheap, trained from scratch. Either change should be re-validated end-to-end
+  in the full model before adoption.
+- The current **tubelet-transformer** default is a reasonable
+  cost/quality middle ground for the local branch.
+
+## Caveats
+
+- **Local-only numbers.** These isolate the spatial module and are not directly
+  comparable to the full model; the best path is to re-run the top encoders
+  (r3d_18, ViViT) inside the full two-branch model.
+- **Single seed, small val set (280).** The from-scratch encoders cluster
+  within run-to-run noise of each other (F1 0.85–0.89); the pretrained-vs-scratch
+  gap and the precision differences are large and robust.
+- `r3d_18` ran at batch size 8 (vs 16 for the others) for memory; this affects
+  only optimisation noise, not the reported FLOPs/latency.
+
+## Reproduce
+
+```bash
+# Tubelet-transformer baseline (local-only)
+uv run dvc repro train_ablation_no_temporal evaluate_ablation_no_temporal
+# Encoder variants
+uv run dvc repro train_spatial evaluate_spatial
+```
+
+Per-variant metrics, confusion matrices, and PR/ROC curves are written to
+`data/08_reporting/spatial_<enc>/{train,val}/` (and
+`data/08_reporting/ablation_no_temporal/` for the tubelet-transformer baseline).
diff --git a/experiments/temporal-models/tube-multiscale-fusion/TEMPORAL_COMPARISON.md b/experiments/temporal-models/tube-multiscale-fusion/TEMPORAL_COMPARISON.md
new file mode 100644
index 00000000..a136c3a7
--- /dev/null
+++ b/experiments/temporal-models/tube-multiscale-fusion/TEMPORAL_COMPARISON.md
@@ -0,0 +1,130 @@
+# Temporal-module comparison — tube-multiscale-fusion
+
+The [ablation study](ABLATIONS.md) showed the **temporal module** (the global
+branch that aggregates the 16 per-frame DINOv2 embeddings into one sequence
+vector) is by far the most important component. This document compares
+different architectures for that aggregator.
+
+## Setup
+
+To isolate the temporal module, all runs use the **global-only** setting
+(`ablation_variant: no_spatial`): DINOv2 ViT-S/14 per-frame (last block
+fine-tuned) → **aggregator** → MLP head. No local tube branch, no fusion — so
+the only thing that varies is how the `(B, 16, 384)` per-frame sequence is
+collapsed to a single `(B, 384)` vector. Same data, seed (42), schedule, and
+augmentation throughout. Metrics on the **val split (280 tubes)**, single seed.
+
+The aggregator takes `(B, T, D)` + a frame mask and returns `(B, D)`:
+
+| Aggregator | How it collapses the sequence |
+|------------|-------------------------------|
+| **Transformer** (baseline/default) | Learnable `[CLS]` + positional embeddings + 2-layer Transformer encoder; CLS readout. |
+| **LSTM** | `nn.LSTM` over packed (masked) frames; last hidden state. |
+| **GRU** | `nn.GRU` over packed (masked) frames; last hidden state. |
+| **MLP** | Zero padded frames, flatten `T×D`, 2-layer MLP. |
+| **1D CNN** | `Conv1d` over the temporal axis (2 layers, k=3) + masked mean pool. |
+| **Linear + weighted avg** | Per-frame `Linear`, learned scalar score per frame, softmax-weighted average. |
+
+## Results (val, 280 tubes)
+
+| Aggregator | F1 | Acc | Prec | Recall | PR-AUC | FP | FN | Recall@FPR=1% | @5% |
+|------------|---:|---:|---:|---:|---:|---:|---:|---:|---:|
+| **LSTM** | **0.9747** | 0.9750 | 0.9507 | **1.0000** | 0.9930 | 7 | **0** | 0.867 | **1.000** |
+| **MLP** | 0.9745 | 0.9750 | **0.9571** | 0.9926 | 0.9873 | 6 | 1 | 0.519 | 0.993 |
+| **1D CNN** | 0.9710 | 0.9714 | 0.9504 | 0.9926 | 0.9897 | 7 | 1 | 0.770 | 0.993 |
+| **Transformer** (baseline) | 0.9708 | 0.9714 | 0.9568 | 0.9852 | 0.9901 | 6 | 2 | 0.770 | 0.993 |
+| **GRU** | 0.9673 | 0.9679 | 0.9500 | 0.9852 | **0.9952** | 7 | 2 | **0.889** | **1.000** |
+| **Linear + weighted avg** | 0.9603 | 0.9607 | 0.9366 | 0.9852 | 0.9908 | 9 | 2 | 0.815 | 0.985 |
+
+(sorted by F1)
+
+### Size & compute
+
+| Aggregator | Total params | Trainable | GFLOPs/seq | Latency (ms/seq)¹ |
+|------------|---:|---:|---:|---:|
+| Transformer | 25.3 M | 5.43 M | 196.1 | 8.8 |
+| LSTM | 22.9 M | 3.06 M | 196.0 | 8.9 |
+| GRU | 22.6 M | 2.76 M | 196.0 | 8.9 |
+| MLP | 28.4 M | 8.56 M | 196.0 | 8.7 |
+| 1D CNN | 22.6 M | 2.76 M | 196.0 | 8.7 |
+| Linear + weighted avg | 21.9 M | 2.02 M | 196.0 | 8.8 |
+
+¹ Batch-1, single RTX 4090.
+
+## Findings
+
+### 1. Compute is identical — the aggregator choice is essentially free.
+
+Every variant lands at **~196 GFLOPs and ~8.8 ms/seq**. The 16 per-frame DINOv2
+forward passes dominate so completely that the aggregator (0.15–6.7 M params)
+is a rounding error in both FLOPs and latency. The decision is therefore purely
+about **accuracy and parameter count**, not speed.
+
+### 2. The architecture barely matters — every learned mixer lands in a tight band.
+
+Five of the six aggregators sit within **F1 0.967–0.975** (≈0.8 points). Once
+you have strong per-frame DINOv2 features, *how* you mix them across time is a
+minor lever. The model is robust to this choice.
+
+### 3. …but you do need real temporal mixing — pure weighted averaging is worst.
+
+`Linear + weighted avg`, which has no frame-to-frame interaction (just a learned
+average), is the clear laggard: lowest F1 (0.960) and most false positives (9).
+Collapsing the sequence by a weighted mean discards the *order and dynamics* of
+how the region evolves — exactly the turbulence-vs-drift signal the temporal
+module exists to capture. Some sequential/interaction capacity is needed.
+
+### 4. Recurrent models (GRU/LSTM) are the sweet spot.
+
+- **GRU** gives the best ranking metrics — **PR-AUC 0.9952** and **recall@1%FPR
+  0.889** (the deployment-critical low-false-alarm point) — with the *fewest*
+  trainable params (2.76 M, half the Transformer's 5.43 M). Its lower F1 at the
+  0.5 threshold (0.967) is a thresholding artefact, not a ranking weakness.
+- **LSTM** gives the best raw F1 (0.975) and perfect recall (0 missed smokes),
+  with R@5%FPR = 1.000.
+
+Both recurrent models **edge the Transformer baseline** on this isolated
+temporal task while being smaller.
+
+### 5. MLP is a trap at the strict operating point.
+
+The MLP ties for the best F1 at threshold 0.5 (0.975) but has by far the
+**worst recall@1%FPR (0.519)** and the most trainable params (8.56 M). Its
+scores rank poorly in the low-FPR tail — good "average" accuracy that would
+fail a strict false-alarm budget. Flattening `T×D` also bakes in a fixed
+sequence length and gives no real temporal inductive bias.
+
+### Recommendation
+
+For this architecture, a **GRU** aggregator is the best default: best ranking
+(PR-AUC, recall@low-FPR), half the parameters of the Transformer, and identical
+compute. **LSTM** is the alternative if raw F1 / recall is the target. The
+current **Transformer** default is mid-pack — competitive but not the strongest,
+and the heaviest of the recurrent/CNN options. Avoid **MLP** (poor low-FPR
+behaviour) and **linear weighted-average** (no temporal mixing) for a
+false-alarm-sensitive deployment.
+
+> Note: this comparison is in the global-only setting. In the full two-branch
+> model the cross-attention fusion may interact with the aggregator choice;
+> swapping the production default to GRU should be re-validated end-to-end.
+
+## Caveats
+
+- **Single seed, small val set (280 tubes).** F1 gaps under ~0.005 (e.g.
+  LSTM vs MLP vs 1D CNN) are within run-to-run noise. The recurrent-vs-average
+  gap and the MLP low-FPR weakness are larger and more likely real, but a
+  multi-seed sweep is needed before changing the production default.
+- `recall@FPR=1%` is estimated from only ~145 negatives, so its tail is noisy.
+
+## Reproduce
+
+```bash
+# Transformer baseline (global-only)
+uv run dvc repro train_ablation_no_spatial evaluate_ablation_no_spatial
+# Aggregator variants
+uv run dvc repro train_temporal evaluate_temporal
+```
+
+Per-variant metrics, confusion matrices, and PR/ROC curves are written to
+`data/08_reporting/temporal_<agg>/{train,val}/` (and
+`data/08_reporting/ablation_no_spatial/` for the Transformer baseline).
diff --git a/experiments/temporal-models/tube-multiscale-fusion/configs/.gitkeep b/experiments/temporal-models/tube-multiscale-fusion/configs/.gitkeep
new file mode 100644
index 00000000..e69de29b
diff --git a/experiments/temporal-models/tube-multiscale-fusion/data/01_raw/.gitignore b/experiments/temporal-models/tube-multiscale-fusion/data/01_raw/.gitignore
new file mode 100644
index 00000000..f6335947
--- /dev/null
+++ b/experiments/temporal-models/tube-multiscale-fusion/data/01_raw/.gitignore
@@ -0,0 +1 @@
+/models
diff --git a/experiments/temporal-models/tube-multiscale-fusion/data/01_raw/.gitkeep b/experiments/temporal-models/tube-multiscale-fusion/data/01_raw/.gitkeep
new file mode 100644
index 00000000..e69de29b
diff --git a/experiments/temporal-models/tube-multiscale-fusion/data/01_raw/datasets/.gitignore b/experiments/temporal-models/tube-multiscale-fusion/data/01_raw/datasets/.gitignore
new file mode 100644
index 00000000..ef615c5d
--- /dev/null
+++ b/experiments/temporal-models/tube-multiscale-fusion/data/01_raw/datasets/.gitignore
@@ -0,0 +1,2 @@
+/val
+/train
diff --git a/experiments/temporal-models/tube-multiscale-fusion/data/02_intermediate/.gitkeep b/experiments/temporal-models/tube-multiscale-fusion/data/02_intermediate/.gitkeep
new file mode 100644
index 00000000..e69de29b
diff --git a/experiments/temporal-models/tube-multiscale-fusion/data/03_primary/.gitkeep b/experiments/temporal-models/tube-multiscale-fusion/data/03_primary/.gitkeep
new file mode 100644
index 00000000..e69de29b
diff --git a/experiments/temporal-models/tube-multiscale-fusion/data/03_primary/tubes/.gitignore b/experiments/temporal-models/tube-multiscale-fusion/data/03_primary/tubes/.gitignore
new file mode 100644
index 00000000..ef615c5d
--- /dev/null
+++ b/experiments/temporal-models/tube-multiscale-fusion/data/03_primary/tubes/.gitignore
@@ -0,0 +1,2 @@
+/val
+/train
diff --git a/experiments/temporal-models/tube-multiscale-fusion/data/04_feature/.gitkeep b/experiments/temporal-models/tube-multiscale-fusion/data/04_feature/.gitkeep
new file mode 100644
index 00000000..e69de29b
diff --git a/experiments/temporal-models/tube-multiscale-fusion/data/05_model_input/.gitignore b/experiments/temporal-models/tube-multiscale-fusion/data/05_model_input/.gitignore
new file mode 100644
index 00000000..a5d9d98f
--- /dev/null
+++ b/experiments/temporal-models/tube-multiscale-fusion/data/05_model_input/.gitignore
@@ -0,0 +1,2 @@
+/train
+/val
diff --git a/experiments/temporal-models/tube-multiscale-fusion/data/05_model_input/.gitkeep b/experiments/temporal-models/tube-multiscale-fusion/data/05_model_input/.gitkeep
new file mode 100644
index 00000000..e69de29b
diff --git a/experiments/temporal-models/tube-multiscale-fusion/data/06_models/.gitignore b/experiments/temporal-models/tube-multiscale-fusion/data/06_models/.gitignore
new file mode 100644
index 00000000..8aecc110
--- /dev/null
+++ b/experiments/temporal-models/tube-multiscale-fusion/data/06_models/.gitignore
@@ -0,0 +1,24 @@
+/dinov2_multiscale
+/dinov2_multiscale_spatial_2x2
+/dinov2_multiscale_spatial_8x8
+/dinov2_multiscale_temporal_stride1
+/dinov2_multiscale_temporal_len8
+/dinov2_multiscale_spatial_4x4
+/ablation_resnet3d
+/ablation_no_temporal
+/ablation_weighted_mean
+/ablation_no_spatial
+/temporal_gru
+/temporal_conv1d
+/temporal_lstm
+/temporal_linear_wavg
+/temporal_mlp
+/spatial_tsm
+/spatial_convlstm
+/spatial_resnet3d
+/spatial_vivit
+/full_resnet3d_transformer
+/full_vivit_lstm
+/full_vivit_transformer
+/full_resnet3d_lstm
+/full_tubelet_lstm
diff --git a/experiments/temporal-models/tube-multiscale-fusion/data/06_models/.gitkeep b/experiments/temporal-models/tube-multiscale-fusion/data/06_models/.gitkeep
new file mode 100644
index 00000000..e69de29b
diff --git a/experiments/temporal-models/tube-multiscale-fusion/data/07_model_output/.gitkeep b/experiments/temporal-models/tube-multiscale-fusion/data/07_model_output/.gitkeep
new file mode 100644
index 00000000..e69de29b
diff --git a/experiments/temporal-models/tube-multiscale-fusion/data/07_model_output/dinov2_multiscale/.gitignore b/experiments/temporal-models/tube-multiscale-fusion/data/07_model_output/dinov2_multiscale/.gitignore
new file mode 100644
index 00000000..91b12e83
--- /dev/null
+++ b/experiments/temporal-models/tube-multiscale-fusion/data/07_model_output/dinov2_multiscale/.gitignore
@@ -0,0 +1 @@
+/model_package.zip
diff --git a/experiments/temporal-models/tube-multiscale-fusion/data/08_reporting/.gitkeep b/experiments/temporal-models/tube-multiscale-fusion/data/08_reporting/.gitkeep
new file mode 100644
index 00000000..e69de29b
diff --git a/experiments/temporal-models/tube-multiscale-fusion/data/08_reporting/ablation_no_spatial/.gitignore b/experiments/temporal-models/tube-multiscale-fusion/data/08_reporting/ablation_no_spatial/.gitignore
new file mode 100644
index 00000000..ef615c5d
--- /dev/null
+++ b/experiments/temporal-models/tube-multiscale-fusion/data/08_reporting/ablation_no_spatial/.gitignore
@@ -0,0 +1,2 @@
+/val
+/train
diff --git a/experiments/temporal-models/tube-multiscale-fusion/data/08_reporting/ablation_no_temporal/.gitignore b/experiments/temporal-models/tube-multiscale-fusion/data/08_reporting/ablation_no_temporal/.gitignore
new file mode 100644
index 00000000..ef615c5d
--- /dev/null
+++ b/experiments/temporal-models/tube-multiscale-fusion/data/08_reporting/ablation_no_temporal/.gitignore
@@ -0,0 +1,2 @@
+/val
+/train
diff --git a/experiments/temporal-models/tube-multiscale-fusion/data/08_reporting/ablation_weighted_mean/.gitignore b/experiments/temporal-models/tube-multiscale-fusion/data/08_reporting/ablation_weighted_mean/.gitignore
new file mode 100644
index 00000000..ef615c5d
--- /dev/null
+++ b/experiments/temporal-models/tube-multiscale-fusion/data/08_reporting/ablation_weighted_mean/.gitignore
@@ -0,0 +1,2 @@
+/val
+/train
diff --git a/experiments/temporal-models/tube-multiscale-fusion/data/08_reporting/dinov2_multiscale/.gitignore b/experiments/temporal-models/tube-multiscale-fusion/data/08_reporting/dinov2_multiscale/.gitignore
new file mode 100644
index 00000000..a5d9d98f
--- /dev/null
+++ b/experiments/temporal-models/tube-multiscale-fusion/data/08_reporting/dinov2_multiscale/.gitignore
@@ -0,0 +1,2 @@
+/train
+/val
diff --git a/experiments/temporal-models/tube-multiscale-fusion/data/08_reporting/dinov2_multiscale_benchmark/benchmark.json b/experiments/temporal-models/tube-multiscale-fusion/data/08_reporting/dinov2_multiscale_benchmark/benchmark.json
new file mode 100644
index 00000000..80f4e0ad
--- /dev/null
+++ b/experiments/temporal-models/tube-multiscale-fusion/data/08_reporting/dinov2_multiscale_benchmark/benchmark.json
@@ -0,0 +1,41 @@
+{
+  "hardware": {
+    "gpu": "NVIDIA GeForce RTX 4090",
+    "cpu_threads": 24
+  },
+  "recall_at_fpr": {
+    "recall@fpr=0.01": 0.8740740740740741,
+    "recall@fpr=0.05": 1.0,
+    "recall@fpr=0.1": 1.0
+  },
+  "model_size": {
+    "total_params": 36435201,
+    "total_params_millions": 36.44,
+    "trainable_params": 16581633,
+    "trainable_params_millions": 16.58,
+    "frozen_params_millions": 19.85,
+    "flops_per_sequence": 226548674048,
+    "gflops_per_sequence": 226.5,
+    "gflops_per_frame": 14.16
+  },
+  "time_to_detection": {
+    "n_positive": 135,
+    "n_detected": 135,
+    "n_never_fired": 0,
+    "median_frames_to_detect": 2.0,
+    "mean_frames_to_detect": 2.074074074074074,
+    "median_seconds_to_detect": 30.0,
+    "mean_seconds_to_detect": 32.22222222222222,
+    "frame_dt_seconds": 30.0
+  },
+  "latency_gpu": {
+    "ms_per_sequence_mean": 10.671932203695178,
+    "ms_per_sequence_std": 0.0240137440704318,
+    "ms_per_frame_mean": 0.6669957627309486
+  },
+  "latency_cpu": {
+    "ms_per_sequence_mean": 286.6578564203034,
+    "ms_per_sequence_std": 0.829664835793929,
+    "ms_per_frame_mean": 17.916116026268963
+  }
+}
\ No newline at end of file
diff --git a/experiments/temporal-models/tube-multiscale-fusion/data/08_reporting/dinov2_multiscale_spatial_4x4/.gitignore b/experiments/temporal-models/tube-multiscale-fusion/data/08_reporting/dinov2_multiscale_spatial_4x4/.gitignore
new file mode 100644
index 00000000..a5d9d98f
--- /dev/null
+++ b/experiments/temporal-models/tube-multiscale-fusion/data/08_reporting/dinov2_multiscale_spatial_4x4/.gitignore
@@ -0,0 +1,2 @@
+/train
+/val
diff --git a/experiments/temporal-models/tube-multiscale-fusion/data/08_reporting/dinov2_multiscale_spatial_8x8/.gitignore b/experiments/temporal-models/tube-multiscale-fusion/data/08_reporting/dinov2_multiscale_spatial_8x8/.gitignore
new file mode 100644
index 00000000..a5d9d98f
--- /dev/null
+++ b/experiments/temporal-models/tube-multiscale-fusion/data/08_reporting/dinov2_multiscale_spatial_8x8/.gitignore
@@ -0,0 +1,2 @@
+/train
+/val
diff --git a/experiments/temporal-models/tube-multiscale-fusion/data/08_reporting/dinov2_multiscale_temporal_len8/.gitignore b/experiments/temporal-models/tube-multiscale-fusion/data/08_reporting/dinov2_multiscale_temporal_len8/.gitignore
new file mode 100644
index 00000000..a5d9d98f
--- /dev/null
+++ b/experiments/temporal-models/tube-multiscale-fusion/data/08_reporting/dinov2_multiscale_temporal_len8/.gitignore
@@ -0,0 +1,2 @@
+/train
+/val
diff --git a/experiments/temporal-models/tube-multiscale-fusion/data/08_reporting/dinov2_multiscale_temporal_stride1/.gitignore b/experiments/temporal-models/tube-multiscale-fusion/data/08_reporting/dinov2_multiscale_temporal_stride1/.gitignore
new file mode 100644
index 00000000..a5d9d98f
--- /dev/null
+++ b/experiments/temporal-models/tube-multiscale-fusion/data/08_reporting/dinov2_multiscale_temporal_stride1/.gitignore
@@ -0,0 +1,2 @@
+/train
+/val
diff --git a/experiments/temporal-models/tube-multiscale-fusion/data/08_reporting/full_resnet3d_lstm/.gitignore b/experiments/temporal-models/tube-multiscale-fusion/data/08_reporting/full_resnet3d_lstm/.gitignore
new file mode 100644
index 00000000..a5d9d98f
--- /dev/null
+++ b/experiments/temporal-models/tube-multiscale-fusion/data/08_reporting/full_resnet3d_lstm/.gitignore
@@ -0,0 +1,2 @@
+/train
+/val
diff --git a/experiments/temporal-models/tube-multiscale-fusion/data/08_reporting/full_resnet3d_transformer/.gitignore b/experiments/temporal-models/tube-multiscale-fusion/data/08_reporting/full_resnet3d_transformer/.gitignore
new file mode 100644
index 00000000..ef615c5d
--- /dev/null
+++ b/experiments/temporal-models/tube-multiscale-fusion/data/08_reporting/full_resnet3d_transformer/.gitignore
@@ -0,0 +1,2 @@
+/val
+/train
diff --git a/experiments/temporal-models/tube-multiscale-fusion/data/08_reporting/full_tubelet_lstm/.gitignore b/experiments/temporal-models/tube-multiscale-fusion/data/08_reporting/full_tubelet_lstm/.gitignore
new file mode 100644
index 00000000..a5d9d98f
--- /dev/null
+++ b/experiments/temporal-models/tube-multiscale-fusion/data/08_reporting/full_tubelet_lstm/.gitignore
@@ -0,0 +1,2 @@
+/train
+/val
diff --git a/experiments/temporal-models/tube-multiscale-fusion/data/08_reporting/full_vivit_lstm/.gitignore b/experiments/temporal-models/tube-multiscale-fusion/data/08_reporting/full_vivit_lstm/.gitignore
new file mode 100644
index 00000000..ef615c5d
--- /dev/null
+++ b/experiments/temporal-models/tube-multiscale-fusion/data/08_reporting/full_vivit_lstm/.gitignore
@@ -0,0 +1,2 @@
+/val
+/train
diff --git a/experiments/temporal-models/tube-multiscale-fusion/data/08_reporting/full_vivit_transformer/.gitignore b/experiments/temporal-models/tube-multiscale-fusion/data/08_reporting/full_vivit_transformer/.gitignore
new file mode 100644
index 00000000..a5d9d98f
--- /dev/null
+++ b/experiments/temporal-models/tube-multiscale-fusion/data/08_reporting/full_vivit_transformer/.gitignore
@@ -0,0 +1,2 @@
+/train
+/val
diff --git a/experiments/temporal-models/tube-multiscale-fusion/data/08_reporting/spatial_convlstm/.gitignore b/experiments/temporal-models/tube-multiscale-fusion/data/08_reporting/spatial_convlstm/.gitignore
new file mode 100644
index 00000000..a5d9d98f
--- /dev/null
+++ b/experiments/temporal-models/tube-multiscale-fusion/data/08_reporting/spatial_convlstm/.gitignore
@@ -0,0 +1,2 @@
+/train
+/val
diff --git a/experiments/temporal-models/tube-multiscale-fusion/data/08_reporting/spatial_resnet3d/.gitignore b/experiments/temporal-models/tube-multiscale-fusion/data/08_reporting/spatial_resnet3d/.gitignore
new file mode 100644
index 00000000..ef615c5d
--- /dev/null
+++ b/experiments/temporal-models/tube-multiscale-fusion/data/08_reporting/spatial_resnet3d/.gitignore
@@ -0,0 +1,2 @@
+/val
+/train
diff --git a/experiments/temporal-models/tube-multiscale-fusion/data/08_reporting/spatial_tsm/.gitignore b/experiments/temporal-models/tube-multiscale-fusion/data/08_reporting/spatial_tsm/.gitignore
new file mode 100644
index 00000000..ef615c5d
--- /dev/null
+++ b/experiments/temporal-models/tube-multiscale-fusion/data/08_reporting/spatial_tsm/.gitignore
@@ -0,0 +1,2 @@
+/val
+/train
diff --git a/experiments/temporal-models/tube-multiscale-fusion/data/08_reporting/spatial_vivit/.gitignore b/experiments/temporal-models/tube-multiscale-fusion/data/08_reporting/spatial_vivit/.gitignore
new file mode 100644
index 00000000..a5d9d98f
--- /dev/null
+++ b/experiments/temporal-models/tube-multiscale-fusion/data/08_reporting/spatial_vivit/.gitignore
@@ -0,0 +1,2 @@
+/train
+/val
diff --git a/experiments/temporal-models/tube-multiscale-fusion/data/08_reporting/temporal_conv1d/.gitignore b/experiments/temporal-models/tube-multiscale-fusion/data/08_reporting/temporal_conv1d/.gitignore
new file mode 100644
index 00000000..ef615c5d
--- /dev/null
+++ b/experiments/temporal-models/tube-multiscale-fusion/data/08_reporting/temporal_conv1d/.gitignore
@@ -0,0 +1,2 @@
+/val
+/train
diff --git a/experiments/temporal-models/tube-multiscale-fusion/data/08_reporting/temporal_gru/.gitignore b/experiments/temporal-models/tube-multiscale-fusion/data/08_reporting/temporal_gru/.gitignore
new file mode 100644
index 00000000..ef615c5d
--- /dev/null
+++ b/experiments/temporal-models/tube-multiscale-fusion/data/08_reporting/temporal_gru/.gitignore
@@ -0,0 +1,2 @@
+/val
+/train
diff --git a/experiments/temporal-models/tube-multiscale-fusion/data/08_reporting/temporal_linear_wavg/.gitignore b/experiments/temporal-models/tube-multiscale-fusion/data/08_reporting/temporal_linear_wavg/.gitignore
new file mode 100644
index 00000000..ef615c5d
--- /dev/null
+++ b/experiments/temporal-models/tube-multiscale-fusion/data/08_reporting/temporal_linear_wavg/.gitignore
@@ -0,0 +1,2 @@
+/val
+/train
diff --git a/experiments/temporal-models/tube-multiscale-fusion/data/08_reporting/temporal_lstm/.gitignore b/experiments/temporal-models/tube-multiscale-fusion/data/08_reporting/temporal_lstm/.gitignore
new file mode 100644
index 00000000..a5d9d98f
--- /dev/null
+++ b/experiments/temporal-models/tube-multiscale-fusion/data/08_reporting/temporal_lstm/.gitignore
@@ -0,0 +1,2 @@
+/train
+/val
diff --git a/experiments/temporal-models/tube-multiscale-fusion/data/08_reporting/temporal_mlp/.gitignore b/experiments/temporal-models/tube-multiscale-fusion/data/08_reporting/temporal_mlp/.gitignore
new file mode 100644
index 00000000..a5d9d98f
--- /dev/null
+++ b/experiments/temporal-models/tube-multiscale-fusion/data/08_reporting/temporal_mlp/.gitignore
@@ -0,0 +1,2 @@
+/train
+/val
diff --git a/experiments/temporal-models/tube-multiscale-fusion/dvc.lock b/experiments/temporal-models/tube-multiscale-fusion/dvc.lock
new file mode 100644
index 00000000..88e52f04
--- /dev/null
+++ b/experiments/temporal-models/tube-multiscale-fusion/dvc.lock
@@ -0,0 +1,7319 @@
+schema: '2.0'
+stages:
+  truncate@val:
+    cmd: uv run python scripts/truncate_sequences.py --input-dir 
+      data/01_raw/datasets_full/val --output-dir data/01_raw/datasets/val 
+      --max-frames 16
+    deps:
+    - path: data/01_raw/datasets_full/val
+      hash: md5
+      md5: b58404e2160d272fd736fad3aa561223.dir
+      size: 668383256
+      nfiles: 13044
+    - path: scripts/truncate_sequences.py
+      hash: md5
+      md5: 50ac7865933889df116cf61d7f35bc63
+      size: 2495
+    params:
+      params.yaml:
+        truncate.max_frames: 16
+    outs:
+    - path: data/01_raw/datasets/val
+      hash: md5
+      md5: 2ee83a5a253a8c7c7f44e3415c74d7d3.dir
+      size: 385718788
+      nfiles: 7594
+  truncate@train:
+    cmd: uv run python scripts/truncate_sequences.py --input-dir 
+      data/01_raw/datasets_full/train --output-dir data/01_raw/datasets/train 
+      --max-frames 16
+    deps:
+    - path: data/01_raw/datasets_full/train
+      hash: md5
+      md5: f02174099bc4dc115dad2b69d3b4e78e.dir
+      size: 7543045856
+      nfiles: 142052
+    - path: scripts/truncate_sequences.py
+      hash: md5
+      md5: 50ac7865933889df116cf61d7f35bc63
+      size: 2495
+    params:
+      params.yaml:
+        truncate.max_frames: 16
+    outs:
+    - path: data/01_raw/datasets/train
+      hash: md5
+      md5: 2cf294bcec0a832c25a2d1ffa086304c.dir
+      size: 4132370460
+      nfiles: 77646
+  build_tubes@val:
+    cmd: uv run python scripts/build_tubes.py --input-dir 
+      data/01_raw/datasets/val --output-dir data/03_primary/tubes/val 
+      --iou-threshold 0.2 --max-misses 2 --min-tube-length 4 
+      --min-detected-entries 2
+    deps:
+    - path: data/01_raw/datasets/val
+      hash: md5
+      md5: 2ee83a5a253a8c7c7f44e3415c74d7d3.dir
+      size: 385718788
+      nfiles: 7594
+    - path: scripts/build_tubes.py
+      hash: md5
+      md5: 1553153e36118e5c306172b5cb371daa
+      size: 5710
+    - path: src/tube_multiscale_fusion/data.py
+      hash: md5
+      md5: b907e5a0baedb93775446678c2cce034
+      size: 5078
+    - path: src/tube_multiscale_fusion/tubes.py
+      hash: md5
+      md5: 2f6f8e5e9ff7342a45ddaec8d0bedc1f
+      size: 11706
+    - path: src/tube_multiscale_fusion/types.py
+      hash: md5
+      md5: 71917e49ebe4659d6b1f5cb0f29a5747
+      size: 1693
+    params:
+      params.yaml:
+        build_tubes:
+          min_tube_length: 4
+          min_detected_entries: 2
+        tubes:
+          iou_threshold: 0.2
+          max_misses: 2
+    outs:
+    - path: data/03_primary/tubes/val
+      hash: md5
+      md5: 162ea13bca9521cf8920324caa6a2aaa.dir
+      size: 910719
+      nfiles: 281
+  build_tubes@train:
+    cmd: uv run python scripts/build_tubes.py --input-dir 
+      data/01_raw/datasets/train --output-dir data/03_primary/tubes/train 
+      --iou-threshold 0.2 --max-misses 2 --min-tube-length 4 
+      --min-detected-entries 2
+    deps:
+    - path: data/01_raw/datasets/train
+      hash: md5
+      md5: 2cf294bcec0a832c25a2d1ffa086304c.dir
+      size: 4132370460
+      nfiles: 77646
+    - path: scripts/build_tubes.py
+      hash: md5
+      md5: 1553153e36118e5c306172b5cb371daa
+      size: 5710
+    - path: src/tube_multiscale_fusion/data.py
+      hash: md5
+      md5: b907e5a0baedb93775446678c2cce034
+      size: 5078
+    - path: src/tube_multiscale_fusion/tubes.py
+      hash: md5
+      md5: 2f6f8e5e9ff7342a45ddaec8d0bedc1f
+      size: 11706
+    - path: src/tube_multiscale_fusion/types.py
+      hash: md5
+      md5: 71917e49ebe4659d6b1f5cb0f29a5747
+      size: 1693
+    params:
+      params.yaml:
+        build_tubes:
+          min_tube_length: 4
+          min_detected_entries: 2
+        tubes:
+          iou_threshold: 0.2
+          max_misses: 2
+    outs:
+    - path: data/03_primary/tubes/train
+      hash: md5
+      md5: 6f5ff8ee6cb88f5b000b08682645721b.dir
+      size: 9636021
+      nfiles: 2832
+  build_model_input@train:
+    cmd: uv run python scripts/build_model_input.py --tubes-dir 
+      data/03_primary/tubes/train --raw-dir data/01_raw/datasets/train 
+      --output-dir data/05_model_input/train --context-factor 1.5 --patch-size 
+      224
+    deps:
+    - path: data/01_raw/datasets/train
+      hash: md5
+      md5: 2cf294bcec0a832c25a2d1ffa086304c.dir
+      size: 4132370460
+      nfiles: 77646
+    - path: data/03_primary/tubes/train
+      hash: md5
+      md5: 6f5ff8ee6cb88f5b000b08682645721b.dir
+      size: 9636021
+      nfiles: 2832
+    - path: scripts/build_model_input.py
+      hash: md5
+      md5: 1bc28c46225152680200e426c5d29c09
+      size: 3715
+    - path: src/tube_multiscale_fusion/model_input.py
+      hash: md5
+      md5: fa9e8bd01b4a5228feeeb8d689405f88
+      size: 3612
+    params:
+      params.yaml:
+        model_input:
+          context_factor: 1.5
+          patch_size: 224
+    outs:
+    - path: data/05_model_input/train
+      hash: md5
+      md5: 840f6c912a4c6ae3103627dd32594242.dir
+      size: 843667150
+      nfiles: 34039
+  build_model_input@val:
+    cmd: uv run python scripts/build_model_input.py --tubes-dir 
+      data/03_primary/tubes/val --raw-dir data/01_raw/datasets/val --output-dir 
+      data/05_model_input/val --context-factor 1.5 --patch-size 224
+    deps:
+    - path: data/01_raw/datasets/val
+      hash: md5
+      md5: 2ee83a5a253a8c7c7f44e3415c74d7d3.dir
+      size: 385718788
+      nfiles: 7594
+    - path: data/03_primary/tubes/val
+      hash: md5
+      md5: 162ea13bca9521cf8920324caa6a2aaa.dir
+      size: 910719
+      nfiles: 281
+    - path: scripts/build_model_input.py
+      hash: md5
+      md5: 1bc28c46225152680200e426c5d29c09
+      size: 3715
+    - path: src/tube_multiscale_fusion/model_input.py
+      hash: md5
+      md5: fa9e8bd01b4a5228feeeb8d689405f88
+      size: 3612
+    params:
+      params.yaml:
+        model_input:
+          context_factor: 1.5
+          patch_size: 224
+    outs:
+    - path: data/05_model_input/val
+      hash: md5
+      md5: f8585e64f607a7a8d7d1651a9cbc930e.dir
+      size: 77725173
+      nfiles: 3219
+  train_dinov2_multiscale:
+    cmd: uv run python scripts/train.py --train-dir data/05_model_input/train 
+      --val-dir data/05_model_input/val --output-dir 
+      data/06_models/dinov2_multiscale --params-path params.yaml --params-key 
+      train_dinov2_multiscale
+    deps:
+    - path: data/05_model_input/train
+      hash: md5
+      md5: 840f6c912a4c6ae3103627dd32594242.dir
+      size: 843667150
+      nfiles: 34039
+    - path: data/05_model_input/val
+      hash: md5
+      md5: f8585e64f607a7a8d7d1651a9cbc930e.dir
+      size: 77725173
+      nfiles: 3219
+    - path: scripts/train.py
+      hash: md5
+      md5: 77eebc50193e30379e877c65e5ea7fa3
+      size: 5443
+    - path: src/tube_multiscale_fusion/augment.py
+      hash: md5
+      md5: f52a42f92caa2fac859bf3a81a18971d
+      size: 9489
+    - path: src/tube_multiscale_fusion/classifier.py
+      hash: md5
+      md5: 4a36dec854fd018990b5bee70bcffd9f
+      size: 3556
+    - path: src/tube_multiscale_fusion/dataset.py
+      hash: md5
+      md5: d7d3e37c27fd25f7612751b3c5869dd2
+      size: 3036
+    - path: src/tube_multiscale_fusion/fusion.py
+      hash: md5
+      md5: dce0c5c806436477dd7f1ff93bd261a5
+      size: 5179
+    - path: src/tube_multiscale_fusion/global_branch.py
+      hash: md5
+      md5: 4cae3af1b1a020daaeeae747bd05f966
+      size: 6341
+    - path: src/tube_multiscale_fusion/lit_module.py
+      hash: md5
+      md5: 4022aa3dc29c80e1a38a23182bec83b3
+      size: 7307
+    - path: src/tube_multiscale_fusion/local_branch.py
+      hash: md5
+      md5: 4a49a7286da503d7e0a21158d1c74295
+      size: 9228
+    params:
+      params.yaml:
+        augment:
+          enabled: true
+          spatial:
+            flip_prob: 0.5
+            rotation_deg: 5.0
+            scale_range:
+            - 0.9
+            - 1.1
+            translate_frac: 0.05
+          photometric:
+            brightness_range:
+            - 0.8
+            - 1.2
+            contrast_range:
+            - 0.8
+            - 1.2
+            saturation_range:
+            - 0.8
+            - 1.2
+          temporal:
+            subseq_prob: 0.5
+            subseq_min_len: 4
+            stride_prob: 0.25
+            frame_drop_prob: 0.15
+            min_valid_after_drop: 4
+        fusion:
+          d_fusion: 384
+          num_layers: 2
+          num_heads: 6
+          ffn_dim: 1536
+          dropout: 0.1
+        global_branch:
+          backbone: vit_small_patch14_dinov2.lvd142m
+          aggregator:
+            num_layers: 2
+            num_heads: 6
+            ffn_dim: 1536
+            dropout: 0.1
+        local_branch:
+          grid_size: 2
+          cell_size: 112
+          tube_length: 4
+          temporal_stride: 2
+          encoder:
+            t_kernel: 2
+            h_patch: 14
+            w_patch: 14
+            embed_dim: 384
+            num_layers: 2
+            num_heads: 6
+            ffn_dim: 1536
+            dropout: 0.1
+        train_dinov2_multiscale:
+          max_frames: 16
+          batch_size: 16
+          num_workers: 4
+          learning_rate: 0.0001
+          weight_decay: 0.05
+          max_epochs: 30
+          early_stop_patience: 5
+          seed: 42
+          head_hidden_dim: 256
+          head_dropout: 0.1
+          use_cosine_warmup: true
+          warmup_frac: 0.05
+          img_size: 224
+          finetune: true
+          finetune_last_n_blocks: 1
+          backbone_lr: 1e-05
+    outs:
+    - path: data/06_models/dinov2_multiscale
+      hash: md5
+      md5: fca6623ba9be2bc9a236b3b9072097a4.dir
+      size: 278692327
+      nfiles: 5
+  evaluate_dinov2_multiscale@train:
+    cmd: uv run python scripts/evaluate.py --data-dir data/05_model_input/train 
+      --checkpoint data/06_models/dinov2_multiscale/best_checkpoint.pt 
+      --output-dir data/08_reporting/dinov2_multiscale/train --params-path 
+      params.yaml --params-key train_dinov2_multiscale
+    deps:
+    - path: data/05_model_input/train
+      hash: md5
+      md5: 840f6c912a4c6ae3103627dd32594242.dir
+      size: 843667150
+      nfiles: 34039
+    - path: data/06_models/dinov2_multiscale
+      hash: md5
+      md5: fca6623ba9be2bc9a236b3b9072097a4.dir
+      size: 278692327
+      nfiles: 5
+    - path: scripts/evaluate.py
+      hash: md5
+      md5: 44cba928007046cc57341989cd752879
+      size: 5231
+    - path: src/tube_multiscale_fusion/classifier.py
+      hash: md5
+      md5: 4a36dec854fd018990b5bee70bcffd9f
+      size: 3556
+    - path: src/tube_multiscale_fusion/dataset.py
+      hash: md5
+      md5: d7d3e37c27fd25f7612751b3c5869dd2
+      size: 3036
+    - path: src/tube_multiscale_fusion/eval_plots.py
+      hash: md5
+      md5: f76a38b124a936129d9e0cc6910b9e33
+      size: 3399
+    - path: src/tube_multiscale_fusion/fusion.py
+      hash: md5
+      md5: dce0c5c806436477dd7f1ff93bd261a5
+      size: 5179
+    - path: src/tube_multiscale_fusion/global_branch.py
+      hash: md5
+      md5: 4cae3af1b1a020daaeeae747bd05f966
+      size: 6341
+    - path: src/tube_multiscale_fusion/lit_module.py
+      hash: md5
+      md5: 4022aa3dc29c80e1a38a23182bec83b3
+      size: 7307
+    - path: src/tube_multiscale_fusion/local_branch.py
+      hash: md5
+      md5: 4a49a7286da503d7e0a21158d1c74295
+      size: 9228
+    params:
+      params.yaml:
+        fusion:
+          d_fusion: 384
+          num_layers: 2
+          num_heads: 6
+          ffn_dim: 1536
+          dropout: 0.1
+        global_branch:
+          backbone: vit_small_patch14_dinov2.lvd142m
+          aggregator:
+            num_layers: 2
+            num_heads: 6
+            ffn_dim: 1536
+            dropout: 0.1
+        local_branch:
+          grid_size: 2
+          cell_size: 112
+          tube_length: 4
+          temporal_stride: 2
+          encoder:
+            t_kernel: 2
+            h_patch: 14
+            w_patch: 14
+            embed_dim: 384
+            num_layers: 2
+            num_heads: 6
+            ffn_dim: 1536
+            dropout: 0.1
+        train_dinov2_multiscale:
+          max_frames: 16
+          batch_size: 16
+          num_workers: 4
+          learning_rate: 0.0001
+          weight_decay: 0.05
+          max_epochs: 30
+          early_stop_patience: 5
+          seed: 42
+          head_hidden_dim: 256
+          head_dropout: 0.1
+          use_cosine_warmup: true
+          warmup_frac: 0.05
+          img_size: 224
+          finetune: true
+          finetune_last_n_blocks: 1
+          backbone_lr: 1e-05
+    outs:
+    - path: data/08_reporting/dinov2_multiscale/train
+      hash: md5
+      md5: 1f25da63bde9a257348ba11ed80e16d3.dir
+      size: 581387
+      nfiles: 6
+  evaluate_dinov2_multiscale@val:
+    cmd: uv run python scripts/evaluate.py --data-dir data/05_model_input/val 
+      --checkpoint data/06_models/dinov2_multiscale/best_checkpoint.pt 
+      --output-dir data/08_reporting/dinov2_multiscale/val --params-path 
+      params.yaml --params-key train_dinov2_multiscale
+    deps:
+    - path: data/05_model_input/val
+      hash: md5
+      md5: f8585e64f607a7a8d7d1651a9cbc930e.dir
+      size: 77725173
+      nfiles: 3219
+    - path: data/06_models/dinov2_multiscale
+      hash: md5
+      md5: fca6623ba9be2bc9a236b3b9072097a4.dir
+      size: 278692327
+      nfiles: 5
+    - path: scripts/evaluate.py
+      hash: md5
+      md5: 44cba928007046cc57341989cd752879
+      size: 5231
+    - path: src/tube_multiscale_fusion/classifier.py
+      hash: md5
+      md5: 4a36dec854fd018990b5bee70bcffd9f
+      size: 3556
+    - path: src/tube_multiscale_fusion/dataset.py
+      hash: md5
+      md5: d7d3e37c27fd25f7612751b3c5869dd2
+      size: 3036
+    - path: src/tube_multiscale_fusion/eval_plots.py
+      hash: md5
+      md5: f76a38b124a936129d9e0cc6910b9e33
+      size: 3399
+    - path: src/tube_multiscale_fusion/fusion.py
+      hash: md5
+      md5: dce0c5c806436477dd7f1ff93bd261a5
+      size: 5179
+    - path: src/tube_multiscale_fusion/global_branch.py
+      hash: md5
+      md5: 4cae3af1b1a020daaeeae747bd05f966
+      size: 6341
+    - path: src/tube_multiscale_fusion/lit_module.py
+      hash: md5
+      md5: 4022aa3dc29c80e1a38a23182bec83b3
+      size: 7307
+    - path: src/tube_multiscale_fusion/local_branch.py
+      hash: md5
+      md5: 4a49a7286da503d7e0a21158d1c74295
+      size: 9228
+    params:
+      params.yaml:
+        fusion:
+          d_fusion: 384
+          num_layers: 2
+          num_heads: 6
+          ffn_dim: 1536
+          dropout: 0.1
+        global_branch:
+          backbone: vit_small_patch14_dinov2.lvd142m
+          aggregator:
+            num_layers: 2
+            num_heads: 6
+            ffn_dim: 1536
+            dropout: 0.1
+        local_branch:
+          grid_size: 2
+          cell_size: 112
+          tube_length: 4
+          temporal_stride: 2
+          encoder:
+            t_kernel: 2
+            h_patch: 14
+            w_patch: 14
+            embed_dim: 384
+            num_layers: 2
+            num_heads: 6
+            ffn_dim: 1536
+            dropout: 0.1
+        train_dinov2_multiscale:
+          max_frames: 16
+          batch_size: 16
+          num_workers: 4
+          learning_rate: 0.0001
+          weight_decay: 0.05
+          max_epochs: 30
+          early_stop_patience: 5
+          seed: 42
+          head_hidden_dim: 256
+          head_dropout: 0.1
+          use_cosine_warmup: true
+          warmup_frac: 0.05
+          img_size: 224
+          finetune: true
+          finetune_last_n_blocks: 1
+          backbone_lr: 1e-05
+    outs:
+    - path: data/08_reporting/dinov2_multiscale/val
+      hash: md5
+      md5: 12a94f552478239031e92fc8b45e01ba.dir
+      size: 144894
+      nfiles: 6
+  train_dinov2_multiscale_sweep@spatial_2x2:
+    cmd: uv run python scripts/train.py --train-dir data/05_model_input/train 
+      --val-dir data/05_model_input/val --output-dir 
+      data/06_models/dinov2_multiscale_spatial_2x2 --params-path params.yaml 
+      --params-key train_dinov2_multiscale_spatial_2x2
+    deps:
+    - path: data/05_model_input/train
+      hash: md5
+      md5: 840f6c912a4c6ae3103627dd32594242.dir
+      size: 843667150
+      nfiles: 34039
+    - path: data/05_model_input/val
+      hash: md5
+      md5: f8585e64f607a7a8d7d1651a9cbc930e.dir
+      size: 77725173
+      nfiles: 3219
+    - path: scripts/train.py
+      hash: md5
+      md5: 2b58199a26cb37fa2f42fbf397bab6b2
+      size: 5441
+    - path: src/tube_multiscale_fusion/augment.py
+      hash: md5
+      md5: f52a42f92caa2fac859bf3a81a18971d
+      size: 9489
+    - path: src/tube_multiscale_fusion/classifier.py
+      hash: md5
+      md5: 4a36dec854fd018990b5bee70bcffd9f
+      size: 3556
+    - path: src/tube_multiscale_fusion/dataset.py
+      hash: md5
+      md5: d7d3e37c27fd25f7612751b3c5869dd2
+      size: 3036
+    - path: src/tube_multiscale_fusion/fusion.py
+      hash: md5
+      md5: dce0c5c806436477dd7f1ff93bd261a5
+      size: 5179
+    - path: src/tube_multiscale_fusion/global_branch.py
+      hash: md5
+      md5: 4cae3af1b1a020daaeeae747bd05f966
+      size: 6341
+    - path: src/tube_multiscale_fusion/lit_module.py
+      hash: md5
+      md5: 4022aa3dc29c80e1a38a23182bec83b3
+      size: 7307
+    - path: src/tube_multiscale_fusion/local_branch.py
+      hash: md5
+      md5: 4a49a7286da503d7e0a21158d1c74295
+      size: 9228
+    params:
+      params.yaml:
+        augment:
+          enabled: true
+          spatial:
+            flip_prob: 0.5
+            rotation_deg: 5.0
+            scale_range:
+            - 0.9
+            - 1.1
+            translate_frac: 0.05
+          photometric:
+            brightness_range:
+            - 0.8
+            - 1.2
+            contrast_range:
+            - 0.8
+            - 1.2
+            saturation_range:
+            - 0.8
+            - 1.2
+          temporal:
+            subseq_prob: 0.5
+            subseq_min_len: 4
+            stride_prob: 0.25
+            frame_drop_prob: 0.15
+            min_valid_after_drop: 4
+        fusion:
+          d_fusion: 384
+          num_layers: 2
+          num_heads: 6
+          ffn_dim: 1536
+          dropout: 0.1
+        global_branch:
+          backbone: vit_small_patch14_dinov2.lvd142m
+          aggregator:
+            num_layers: 2
+            num_heads: 6
+            ffn_dim: 1536
+            dropout: 0.1
+        local_branch_spatial_2x2:
+          grid_size: 2
+          cell_size: 112
+          tube_length: 4
+          temporal_stride: 2
+          encoder:
+            t_kernel: 2
+            h_patch: 14
+            w_patch: 14
+            embed_dim: 384
+            num_layers: 2
+            num_heads: 6
+            ffn_dim: 1536
+            dropout: 0.1
+        train_dinov2_multiscale_spatial_2x2:
+          max_frames: 16
+          batch_size: 16
+          num_workers: 4
+          learning_rate: 0.0001
+          weight_decay: 0.05
+          max_epochs: 30
+          early_stop_patience: 5
+          seed: 42
+          head_hidden_dim: 256
+          head_dropout: 0.1
+          use_cosine_warmup: true
+          warmup_frac: 0.05
+          img_size: 224
+          finetune: true
+          finetune_last_n_blocks: 1
+          backbone_lr: 1e-05
+          local_branch_section: local_branch_spatial_2x2
+    outs:
+    - path: data/06_models/dinov2_multiscale_spatial_2x2
+      hash: md5
+      md5: e53145752327bb1e91450d552d57382f.dir
+      size: 278684379
+      nfiles: 5
+  train_dinov2_multiscale_sweep@spatial_8x8:
+    cmd: uv run python scripts/train.py --train-dir data/05_model_input/train 
+      --val-dir data/05_model_input/val --output-dir 
+      data/06_models/dinov2_multiscale_spatial_8x8 --params-path params.yaml 
+      --params-key train_dinov2_multiscale_spatial_8x8
+    deps:
+    - path: data/05_model_input/train
+      hash: md5
+      md5: 840f6c912a4c6ae3103627dd32594242.dir
+      size: 843667150
+      nfiles: 34039
+    - path: data/05_model_input/val
+      hash: md5
+      md5: f8585e64f607a7a8d7d1651a9cbc930e.dir
+      size: 77725173
+      nfiles: 3219
+    - path: scripts/train.py
+      hash: md5
+      md5: 2b58199a26cb37fa2f42fbf397bab6b2
+      size: 5441
+    - path: src/tube_multiscale_fusion/augment.py
+      hash: md5
+      md5: f52a42f92caa2fac859bf3a81a18971d
+      size: 9489
+    - path: src/tube_multiscale_fusion/classifier.py
+      hash: md5
+      md5: 4a36dec854fd018990b5bee70bcffd9f
+      size: 3556
+    - path: src/tube_multiscale_fusion/dataset.py
+      hash: md5
+      md5: d7d3e37c27fd25f7612751b3c5869dd2
+      size: 3036
+    - path: src/tube_multiscale_fusion/fusion.py
+      hash: md5
+      md5: dce0c5c806436477dd7f1ff93bd261a5
+      size: 5179
+    - path: src/tube_multiscale_fusion/global_branch.py
+      hash: md5
+      md5: 4cae3af1b1a020daaeeae747bd05f966
+      size: 6341
+    - path: src/tube_multiscale_fusion/lit_module.py
+      hash: md5
+      md5: 4022aa3dc29c80e1a38a23182bec83b3
+      size: 7307
+    - path: src/tube_multiscale_fusion/local_branch.py
+      hash: md5
+      md5: 4a49a7286da503d7e0a21158d1c74295
+      size: 9228
+    params:
+      params.yaml:
+        augment:
+          enabled: true
+          spatial:
+            flip_prob: 0.5
+            rotation_deg: 5.0
+            scale_range:
+            - 0.9
+            - 1.1
+            translate_frac: 0.05
+          photometric:
+            brightness_range:
+            - 0.8
+            - 1.2
+            contrast_range:
+            - 0.8
+            - 1.2
+            saturation_range:
+            - 0.8
+            - 1.2
+          temporal:
+            subseq_prob: 0.5
+            subseq_min_len: 4
+            stride_prob: 0.25
+            frame_drop_prob: 0.15
+            min_valid_after_drop: 4
+        fusion:
+          d_fusion: 384
+          num_layers: 2
+          num_heads: 6
+          ffn_dim: 1536
+          dropout: 0.1
+        global_branch:
+          backbone: vit_small_patch14_dinov2.lvd142m
+          aggregator:
+            num_layers: 2
+            num_heads: 6
+            ffn_dim: 1536
+            dropout: 0.1
+        local_branch_spatial_8x8:
+          grid_size: 8
+          cell_size: 28
+          tube_length: 4
+          temporal_stride: 2
+          encoder:
+            t_kernel: 2
+            h_patch: 14
+            w_patch: 14
+            embed_dim: 384
+            num_layers: 2
+            num_heads: 6
+            ffn_dim: 1536
+            dropout: 0.1
+        train_dinov2_multiscale_spatial_8x8:
+          max_frames: 16
+          batch_size: 16
+          num_workers: 4
+          learning_rate: 0.0001
+          weight_decay: 0.05
+          max_epochs: 30
+          early_stop_patience: 5
+          seed: 42
+          head_hidden_dim: 256
+          head_dropout: 0.1
+          use_cosine_warmup: true
+          warmup_frac: 0.05
+          img_size: 224
+          finetune: true
+          finetune_last_n_blocks: 1
+          backbone_lr: 1e-05
+          local_branch_section: local_branch_spatial_8x8
+    outs:
+    - path: data/06_models/dinov2_multiscale_spatial_8x8
+      hash: md5
+      md5: c0cdcc7c374836b24affcfd6ae9481ec.dir
+      size: 278118256
+      nfiles: 5
+  train_dinov2_multiscale_sweep@temporal_stride1:
+    cmd: uv run python scripts/train.py --train-dir data/05_model_input/train 
+      --val-dir data/05_model_input/val --output-dir 
+      data/06_models/dinov2_multiscale_temporal_stride1 --params-path 
+      params.yaml --params-key train_dinov2_multiscale_temporal_stride1
+    deps:
+    - path: data/05_model_input/train
+      hash: md5
+      md5: 840f6c912a4c6ae3103627dd32594242.dir
+      size: 843667150
+      nfiles: 34039
+    - path: data/05_model_input/val
+      hash: md5
+      md5: f8585e64f607a7a8d7d1651a9cbc930e.dir
+      size: 77725173
+      nfiles: 3219
+    - path: scripts/train.py
+      hash: md5
+      md5: 2b58199a26cb37fa2f42fbf397bab6b2
+      size: 5441
+    - path: src/tube_multiscale_fusion/augment.py
+      hash: md5
+      md5: f52a42f92caa2fac859bf3a81a18971d
+      size: 9489
+    - path: src/tube_multiscale_fusion/classifier.py
+      hash: md5
+      md5: 4a36dec854fd018990b5bee70bcffd9f
+      size: 3556
+    - path: src/tube_multiscale_fusion/dataset.py
+      hash: md5
+      md5: d7d3e37c27fd25f7612751b3c5869dd2
+      size: 3036
+    - path: src/tube_multiscale_fusion/fusion.py
+      hash: md5
+      md5: dce0c5c806436477dd7f1ff93bd261a5
+      size: 5179
+    - path: src/tube_multiscale_fusion/global_branch.py
+      hash: md5
+      md5: 4cae3af1b1a020daaeeae747bd05f966
+      size: 6341
+    - path: src/tube_multiscale_fusion/lit_module.py
+      hash: md5
+      md5: 4022aa3dc29c80e1a38a23182bec83b3
+      size: 7307
+    - path: src/tube_multiscale_fusion/local_branch.py
+      hash: md5
+      md5: 4a49a7286da503d7e0a21158d1c74295
+      size: 9228
+    params:
+      params.yaml:
+        augment:
+          enabled: true
+          spatial:
+            flip_prob: 0.5
+            rotation_deg: 5.0
+            scale_range:
+            - 0.9
+            - 1.1
+            translate_frac: 0.05
+          photometric:
+            brightness_range:
+            - 0.8
+            - 1.2
+            contrast_range:
+            - 0.8
+            - 1.2
+            saturation_range:
+            - 0.8
+            - 1.2
+          temporal:
+            subseq_prob: 0.5
+            subseq_min_len: 4
+            stride_prob: 0.25
+            frame_drop_prob: 0.15
+            min_valid_after_drop: 4
+        fusion:
+          d_fusion: 384
+          num_layers: 2
+          num_heads: 6
+          ffn_dim: 1536
+          dropout: 0.1
+        global_branch:
+          backbone: vit_small_patch14_dinov2.lvd142m
+          aggregator:
+            num_layers: 2
+            num_heads: 6
+            ffn_dim: 1536
+            dropout: 0.1
+        local_branch_temporal_stride1:
+          grid_size: 2
+          cell_size: 112
+          tube_length: 4
+          temporal_stride: 1
+          encoder:
+            t_kernel: 2
+            h_patch: 14
+            w_patch: 14
+            embed_dim: 384
+            num_layers: 2
+            num_heads: 6
+            ffn_dim: 1536
+            dropout: 0.1
+        train_dinov2_multiscale_temporal_stride1:
+          max_frames: 16
+          batch_size: 16
+          num_workers: 4
+          learning_rate: 0.0001
+          weight_decay: 0.05
+          max_epochs: 30
+          early_stop_patience: 5
+          seed: 42
+          head_hidden_dim: 256
+          head_dropout: 0.1
+          use_cosine_warmup: true
+          warmup_frac: 0.05
+          img_size: 224
+          finetune: true
+          finetune_last_n_blocks: 1
+          backbone_lr: 1e-05
+          local_branch_section: local_branch_temporal_stride1
+    outs:
+    - path: data/06_models/dinov2_multiscale_temporal_stride1
+      hash: md5
+      md5: af4d6248690c381f0d28469a6730fac3.dir
+      size: 278234097
+      nfiles: 5
+  train_dinov2_multiscale_sweep@temporal_len8:
+    cmd: uv run python scripts/train.py --train-dir data/05_model_input/train 
+      --val-dir data/05_model_input/val --output-dir 
+      data/06_models/dinov2_multiscale_temporal_len8 --params-path params.yaml 
+      --params-key train_dinov2_multiscale_temporal_len8
+    deps:
+    - path: data/05_model_input/train
+      hash: md5
+      md5: 840f6c912a4c6ae3103627dd32594242.dir
+      size: 843667150
+      nfiles: 34039
+    - path: data/05_model_input/val
+      hash: md5
+      md5: f8585e64f607a7a8d7d1651a9cbc930e.dir
+      size: 77725173
+      nfiles: 3219
+    - path: scripts/train.py
+      hash: md5
+      md5: 2b58199a26cb37fa2f42fbf397bab6b2
+      size: 5441
+    - path: src/tube_multiscale_fusion/augment.py
+      hash: md5
+      md5: f52a42f92caa2fac859bf3a81a18971d
+      size: 9489
+    - path: src/tube_multiscale_fusion/classifier.py
+      hash: md5
+      md5: 4a36dec854fd018990b5bee70bcffd9f
+      size: 3556
+    - path: src/tube_multiscale_fusion/dataset.py
+      hash: md5
+      md5: d7d3e37c27fd25f7612751b3c5869dd2
+      size: 3036
+    - path: src/tube_multiscale_fusion/fusion.py
+      hash: md5
+      md5: dce0c5c806436477dd7f1ff93bd261a5
+      size: 5179
+    - path: src/tube_multiscale_fusion/global_branch.py
+      hash: md5
+      md5: 4cae3af1b1a020daaeeae747bd05f966
+      size: 6341
+    - path: src/tube_multiscale_fusion/lit_module.py
+      hash: md5
+      md5: 4022aa3dc29c80e1a38a23182bec83b3
+      size: 7307
+    - path: src/tube_multiscale_fusion/local_branch.py
+      hash: md5
+      md5: 4a49a7286da503d7e0a21158d1c74295
+      size: 9228
+    params:
+      params.yaml:
+        augment:
+          enabled: true
+          spatial:
+            flip_prob: 0.5
+            rotation_deg: 5.0
+            scale_range:
+            - 0.9
+            - 1.1
+            translate_frac: 0.05
+          photometric:
+            brightness_range:
+            - 0.8
+            - 1.2
+            contrast_range:
+            - 0.8
+            - 1.2
+            saturation_range:
+            - 0.8
+            - 1.2
+          temporal:
+            subseq_prob: 0.5
+            subseq_min_len: 4
+            stride_prob: 0.25
+            frame_drop_prob: 0.15
+            min_valid_after_drop: 4
+        fusion:
+          d_fusion: 384
+          num_layers: 2
+          num_heads: 6
+          ffn_dim: 1536
+          dropout: 0.1
+        global_branch:
+          backbone: vit_small_patch14_dinov2.lvd142m
+          aggregator:
+            num_layers: 2
+            num_heads: 6
+            ffn_dim: 1536
+            dropout: 0.1
+        local_branch_temporal_len8:
+          grid_size: 2
+          cell_size: 112
+          tube_length: 8
+          temporal_stride: 4
+          encoder:
+            t_kernel: 2
+            h_patch: 14
+            w_patch: 14
+            embed_dim: 384
+            num_layers: 2
+            num_heads: 6
+            ffn_dim: 1536
+            dropout: 0.1
+        train_dinov2_multiscale_temporal_len8:
+          max_frames: 16
+          batch_size: 16
+          num_workers: 4
+          learning_rate: 0.0001
+          weight_decay: 0.05
+          max_epochs: 30
+          early_stop_patience: 5
+          seed: 42
+          head_hidden_dim: 256
+          head_dropout: 0.1
+          use_cosine_warmup: true
+          warmup_frac: 0.05
+          img_size: 224
+          finetune: true
+          finetune_last_n_blocks: 1
+          backbone_lr: 1e-05
+          local_branch_section: local_branch_temporal_len8
+    outs:
+    - path: data/06_models/dinov2_multiscale_temporal_len8
+      hash: md5
+      md5: e8669b5629c79a9d377b199e1779e2c1.dir
+      size: 278384216
+      nfiles: 5
+  evaluate_dinov2_multiscale_sweep@spatial_2x2-train:
+    cmd: uv run python scripts/evaluate.py --data-dir data/05_model_input/train 
+      --checkpoint 
+      data/06_models/dinov2_multiscale_spatial_2x2/best_checkpoint.pt 
+      --output-dir data/08_reporting/dinov2_multiscale_spatial_2x2/train 
+      --params-path params.yaml --params-key train_dinov2_multiscale_spatial_2x2
+    deps:
+    - path: data/05_model_input/train
+      hash: md5
+      md5: 840f6c912a4c6ae3103627dd32594242.dir
+      size: 843667150
+      nfiles: 34039
+    - path: data/06_models/dinov2_multiscale_spatial_2x2
+      hash: md5
+      md5: e53145752327bb1e91450d552d57382f.dir
+      size: 278684379
+      nfiles: 5
+    - path: scripts/evaluate.py
+      hash: md5
+      md5: 44cba928007046cc57341989cd752879
+      size: 5231
+    - path: src/tube_multiscale_fusion/classifier.py
+      hash: md5
+      md5: 4a36dec854fd018990b5bee70bcffd9f
+      size: 3556
+    - path: src/tube_multiscale_fusion/dataset.py
+      hash: md5
+      md5: d7d3e37c27fd25f7612751b3c5869dd2
+      size: 3036
+    - path: src/tube_multiscale_fusion/eval_plots.py
+      hash: md5
+      md5: f76a38b124a936129d9e0cc6910b9e33
+      size: 3399
+    - path: src/tube_multiscale_fusion/fusion.py
+      hash: md5
+      md5: dce0c5c806436477dd7f1ff93bd261a5
+      size: 5179
+    - path: src/tube_multiscale_fusion/global_branch.py
+      hash: md5
+      md5: 4cae3af1b1a020daaeeae747bd05f966
+      size: 6341
+    - path: src/tube_multiscale_fusion/lit_module.py
+      hash: md5
+      md5: 4022aa3dc29c80e1a38a23182bec83b3
+      size: 7307
+    - path: src/tube_multiscale_fusion/local_branch.py
+      hash: md5
+      md5: 4a49a7286da503d7e0a21158d1c74295
+      size: 9228
+    params:
+      params.yaml:
+        fusion:
+          d_fusion: 384
+          num_layers: 2
+          num_heads: 6
+          ffn_dim: 1536
+          dropout: 0.1
+        global_branch:
+          backbone: vit_small_patch14_dinov2.lvd142m
+          aggregator:
+            num_layers: 2
+            num_heads: 6
+            ffn_dim: 1536
+            dropout: 0.1
+        local_branch_spatial_2x2:
+          grid_size: 2
+          cell_size: 112
+          tube_length: 4
+          temporal_stride: 2
+          encoder:
+            t_kernel: 2
+            h_patch: 14
+            w_patch: 14
+            embed_dim: 384
+            num_layers: 2
+            num_heads: 6
+            ffn_dim: 1536
+            dropout: 0.1
+        train_dinov2_multiscale_spatial_2x2:
+          max_frames: 16
+          batch_size: 16
+          num_workers: 4
+          learning_rate: 0.0001
+          weight_decay: 0.05
+          max_epochs: 30
+          early_stop_patience: 5
+          seed: 42
+          head_hidden_dim: 256
+          head_dropout: 0.1
+          use_cosine_warmup: true
+          warmup_frac: 0.05
+          img_size: 224
+          finetune: true
+          finetune_last_n_blocks: 1
+          backbone_lr: 1e-05
+          local_branch_section: local_branch_spatial_2x2
+    outs:
+    - path: data/08_reporting/dinov2_multiscale_spatial_2x2/train
+      hash: md5
+      md5: edfa5cacedfb9a1c20e83de0d9483cfe.dir
+      size: 585614
+      nfiles: 6
+  evaluate_dinov2_multiscale_sweep@spatial_2x2-val:
+    cmd: uv run python scripts/evaluate.py --data-dir data/05_model_input/val 
+      --checkpoint 
+      data/06_models/dinov2_multiscale_spatial_2x2/best_checkpoint.pt 
+      --output-dir data/08_reporting/dinov2_multiscale_spatial_2x2/val 
+      --params-path params.yaml --params-key train_dinov2_multiscale_spatial_2x2
+    deps:
+    - path: data/05_model_input/val
+      hash: md5
+      md5: f8585e64f607a7a8d7d1651a9cbc930e.dir
+      size: 77725173
+      nfiles: 3219
+    - path: data/06_models/dinov2_multiscale_spatial_2x2
+      hash: md5
+      md5: e53145752327bb1e91450d552d57382f.dir
+      size: 278684379
+      nfiles: 5
+    - path: scripts/evaluate.py
+      hash: md5
+      md5: 44cba928007046cc57341989cd752879
+      size: 5231
+    - path: src/tube_multiscale_fusion/classifier.py
+      hash: md5
+      md5: 4a36dec854fd018990b5bee70bcffd9f
+      size: 3556
+    - path: src/tube_multiscale_fusion/dataset.py
+      hash: md5
+      md5: d7d3e37c27fd25f7612751b3c5869dd2
+      size: 3036
+    - path: src/tube_multiscale_fusion/eval_plots.py
+      hash: md5
+      md5: f76a38b124a936129d9e0cc6910b9e33
+      size: 3399
+    - path: src/tube_multiscale_fusion/fusion.py
+      hash: md5
+      md5: dce0c5c806436477dd7f1ff93bd261a5
+      size: 5179
+    - path: src/tube_multiscale_fusion/global_branch.py
+      hash: md5
+      md5: 4cae3af1b1a020daaeeae747bd05f966
+      size: 6341
+    - path: src/tube_multiscale_fusion/lit_module.py
+      hash: md5
+      md5: 4022aa3dc29c80e1a38a23182bec83b3
+      size: 7307
+    - path: src/tube_multiscale_fusion/local_branch.py
+      hash: md5
+      md5: 4a49a7286da503d7e0a21158d1c74295
+      size: 9228
+    params:
+      params.yaml:
+        fusion:
+          d_fusion: 384
+          num_layers: 2
+          num_heads: 6
+          ffn_dim: 1536
+          dropout: 0.1
+        global_branch:
+          backbone: vit_small_patch14_dinov2.lvd142m
+          aggregator:
+            num_layers: 2
+            num_heads: 6
+            ffn_dim: 1536
+            dropout: 0.1
+        local_branch_spatial_2x2:
+          grid_size: 2
+          cell_size: 112
+          tube_length: 4
+          temporal_stride: 2
+          encoder:
+            t_kernel: 2
+            h_patch: 14
+            w_patch: 14
+            embed_dim: 384
+            num_layers: 2
+            num_heads: 6
+            ffn_dim: 1536
+            dropout: 0.1
+        train_dinov2_multiscale_spatial_2x2:
+          max_frames: 16
+          batch_size: 16
+          num_workers: 4
+          learning_rate: 0.0001
+          weight_decay: 0.05
+          max_epochs: 30
+          early_stop_patience: 5
+          seed: 42
+          head_hidden_dim: 256
+          head_dropout: 0.1
+          use_cosine_warmup: true
+          warmup_frac: 0.05
+          img_size: 224
+          finetune: true
+          finetune_last_n_blocks: 1
+          backbone_lr: 1e-05
+          local_branch_section: local_branch_spatial_2x2
+    outs:
+    - path: data/08_reporting/dinov2_multiscale_spatial_2x2/val
+      hash: md5
+      md5: cbbb27b14880e62866f2ce722b5e31b6.dir
+      size: 149127
+      nfiles: 6
+  evaluate_dinov2_multiscale_sweep@spatial_8x8-train:
+    cmd: uv run python scripts/evaluate.py --data-dir data/05_model_input/train 
+      --checkpoint 
+      data/06_models/dinov2_multiscale_spatial_8x8/best_checkpoint.pt 
+      --output-dir data/08_reporting/dinov2_multiscale_spatial_8x8/train 
+      --params-path params.yaml --params-key train_dinov2_multiscale_spatial_8x8
+    deps:
+    - path: data/05_model_input/train
+      hash: md5
+      md5: 840f6c912a4c6ae3103627dd32594242.dir
+      size: 843667150
+      nfiles: 34039
+    - path: data/06_models/dinov2_multiscale_spatial_8x8
+      hash: md5
+      md5: c0cdcc7c374836b24affcfd6ae9481ec.dir
+      size: 278118256
+      nfiles: 5
+    - path: scripts/evaluate.py
+      hash: md5
+      md5: 44cba928007046cc57341989cd752879
+      size: 5231
+    - path: src/tube_multiscale_fusion/classifier.py
+      hash: md5
+      md5: 4a36dec854fd018990b5bee70bcffd9f
+      size: 3556
+    - path: src/tube_multiscale_fusion/dataset.py
+      hash: md5
+      md5: d7d3e37c27fd25f7612751b3c5869dd2
+      size: 3036
+    - path: src/tube_multiscale_fusion/eval_plots.py
+      hash: md5
+      md5: f76a38b124a936129d9e0cc6910b9e33
+      size: 3399
+    - path: src/tube_multiscale_fusion/fusion.py
+      hash: md5
+      md5: dce0c5c806436477dd7f1ff93bd261a5
+      size: 5179
+    - path: src/tube_multiscale_fusion/global_branch.py
+      hash: md5
+      md5: 4cae3af1b1a020daaeeae747bd05f966
+      size: 6341
+    - path: src/tube_multiscale_fusion/lit_module.py
+      hash: md5
+      md5: 4022aa3dc29c80e1a38a23182bec83b3
+      size: 7307
+    - path: src/tube_multiscale_fusion/local_branch.py
+      hash: md5
+      md5: 4a49a7286da503d7e0a21158d1c74295
+      size: 9228
+    params:
+      params.yaml:
+        fusion:
+          d_fusion: 384
+          num_layers: 2
+          num_heads: 6
+          ffn_dim: 1536
+          dropout: 0.1
+        global_branch:
+          backbone: vit_small_patch14_dinov2.lvd142m
+          aggregator:
+            num_layers: 2
+            num_heads: 6
+            ffn_dim: 1536
+            dropout: 0.1
+        local_branch_spatial_8x8:
+          grid_size: 8
+          cell_size: 28
+          tube_length: 4
+          temporal_stride: 2
+          encoder:
+            t_kernel: 2
+            h_patch: 14
+            w_patch: 14
+            embed_dim: 384
+            num_layers: 2
+            num_heads: 6
+            ffn_dim: 1536
+            dropout: 0.1
+        train_dinov2_multiscale_spatial_8x8:
+          max_frames: 16
+          batch_size: 16
+          num_workers: 4
+          learning_rate: 0.0001
+          weight_decay: 0.05
+          max_epochs: 30
+          early_stop_patience: 5
+          seed: 42
+          head_hidden_dim: 256
+          head_dropout: 0.1
+          use_cosine_warmup: true
+          warmup_frac: 0.05
+          img_size: 224
+          finetune: true
+          finetune_last_n_blocks: 1
+          backbone_lr: 1e-05
+          local_branch_section: local_branch_spatial_8x8
+    outs:
+    - path: data/08_reporting/dinov2_multiscale_spatial_8x8/train
+      hash: md5
+      md5: 441101655a51abb16e0cb3f23e9feecf.dir
+      size: 586249
+      nfiles: 6
+  evaluate_dinov2_multiscale_sweep@spatial_8x8-val:
+    cmd: uv run python scripts/evaluate.py --data-dir data/05_model_input/val 
+      --checkpoint 
+      data/06_models/dinov2_multiscale_spatial_8x8/best_checkpoint.pt 
+      --output-dir data/08_reporting/dinov2_multiscale_spatial_8x8/val 
+      --params-path params.yaml --params-key train_dinov2_multiscale_spatial_8x8
+    deps:
+    - path: data/05_model_input/val
+      hash: md5
+      md5: f8585e64f607a7a8d7d1651a9cbc930e.dir
+      size: 77725173
+      nfiles: 3219
+    - path: data/06_models/dinov2_multiscale_spatial_8x8
+      hash: md5
+      md5: c0cdcc7c374836b24affcfd6ae9481ec.dir
+      size: 278118256
+      nfiles: 5
+    - path: scripts/evaluate.py
+      hash: md5
+      md5: 44cba928007046cc57341989cd752879
+      size: 5231
+    - path: src/tube_multiscale_fusion/classifier.py
+      hash: md5
+      md5: 4a36dec854fd018990b5bee70bcffd9f
+      size: 3556
+    - path: src/tube_multiscale_fusion/dataset.py
+      hash: md5
+      md5: d7d3e37c27fd25f7612751b3c5869dd2
+      size: 3036
+    - path: src/tube_multiscale_fusion/eval_plots.py
+      hash: md5
+      md5: f76a38b124a936129d9e0cc6910b9e33
+      size: 3399
+    - path: src/tube_multiscale_fusion/fusion.py
+      hash: md5
+      md5: dce0c5c806436477dd7f1ff93bd261a5
+      size: 5179
+    - path: src/tube_multiscale_fusion/global_branch.py
+      hash: md5
+      md5: 4cae3af1b1a020daaeeae747bd05f966
+      size: 6341
+    - path: src/tube_multiscale_fusion/lit_module.py
+      hash: md5
+      md5: 4022aa3dc29c80e1a38a23182bec83b3
+      size: 7307
+    - path: src/tube_multiscale_fusion/local_branch.py
+      hash: md5
+      md5: 4a49a7286da503d7e0a21158d1c74295
+      size: 9228
+    params:
+      params.yaml:
+        fusion:
+          d_fusion: 384
+          num_layers: 2
+          num_heads: 6
+          ffn_dim: 1536
+          dropout: 0.1
+        global_branch:
+          backbone: vit_small_patch14_dinov2.lvd142m
+          aggregator:
+            num_layers: 2
+            num_heads: 6
+            ffn_dim: 1536
+            dropout: 0.1
+        local_branch_spatial_8x8:
+          grid_size: 8
+          cell_size: 28
+          tube_length: 4
+          temporal_stride: 2
+          encoder:
+            t_kernel: 2
+            h_patch: 14
+            w_patch: 14
+            embed_dim: 384
+            num_layers: 2
+            num_heads: 6
+            ffn_dim: 1536
+            dropout: 0.1
+        train_dinov2_multiscale_spatial_8x8:
+          max_frames: 16
+          batch_size: 16
+          num_workers: 4
+          learning_rate: 0.0001
+          weight_decay: 0.05
+          max_epochs: 30
+          early_stop_patience: 5
+          seed: 42
+          head_hidden_dim: 256
+          head_dropout: 0.1
+          use_cosine_warmup: true
+          warmup_frac: 0.05
+          img_size: 224
+          finetune: true
+          finetune_last_n_blocks: 1
+          backbone_lr: 1e-05
+          local_branch_section: local_branch_spatial_8x8
+    outs:
+    - path: data/08_reporting/dinov2_multiscale_spatial_8x8/val
+      hash: md5
+      md5: 45412d8a41f51be0906abbd1a24943f7.dir
+      size: 147824
+      nfiles: 6
+  evaluate_dinov2_multiscale_sweep@temporal_stride1-train:
+    cmd: uv run python scripts/evaluate.py --data-dir data/05_model_input/train 
+      --checkpoint 
+      data/06_models/dinov2_multiscale_temporal_stride1/best_checkpoint.pt 
+      --output-dir data/08_reporting/dinov2_multiscale_temporal_stride1/train 
+      --params-path params.yaml --params-key 
+      train_dinov2_multiscale_temporal_stride1
+    deps:
+    - path: data/05_model_input/train
+      hash: md5
+      md5: 840f6c912a4c6ae3103627dd32594242.dir
+      size: 843667150
+      nfiles: 34039
+    - path: data/06_models/dinov2_multiscale_temporal_stride1
+      hash: md5
+      md5: af4d6248690c381f0d28469a6730fac3.dir
+      size: 278234097
+      nfiles: 5
+    - path: scripts/evaluate.py
+      hash: md5
+      md5: 44cba928007046cc57341989cd752879
+      size: 5231
+    - path: src/tube_multiscale_fusion/classifier.py
+      hash: md5
+      md5: 4a36dec854fd018990b5bee70bcffd9f
+      size: 3556
+    - path: src/tube_multiscale_fusion/dataset.py
+      hash: md5
+      md5: d7d3e37c27fd25f7612751b3c5869dd2
+      size: 3036
+    - path: src/tube_multiscale_fusion/eval_plots.py
+      hash: md5
+      md5: f76a38b124a936129d9e0cc6910b9e33
+      size: 3399
+    - path: src/tube_multiscale_fusion/fusion.py
+      hash: md5
+      md5: dce0c5c806436477dd7f1ff93bd261a5
+      size: 5179
+    - path: src/tube_multiscale_fusion/global_branch.py
+      hash: md5
+      md5: 4cae3af1b1a020daaeeae747bd05f966
+      size: 6341
+    - path: src/tube_multiscale_fusion/lit_module.py
+      hash: md5
+      md5: 4022aa3dc29c80e1a38a23182bec83b3
+      size: 7307
+    - path: src/tube_multiscale_fusion/local_branch.py
+      hash: md5
+      md5: 4a49a7286da503d7e0a21158d1c74295
+      size: 9228
+    params:
+      params.yaml:
+        fusion:
+          d_fusion: 384
+          num_layers: 2
+          num_heads: 6
+          ffn_dim: 1536
+          dropout: 0.1
+        global_branch:
+          backbone: vit_small_patch14_dinov2.lvd142m
+          aggregator:
+            num_layers: 2
+            num_heads: 6
+            ffn_dim: 1536
+            dropout: 0.1
+        local_branch_temporal_stride1:
+          grid_size: 2
+          cell_size: 112
+          tube_length: 4
+          temporal_stride: 1
+          encoder:
+            t_kernel: 2
+            h_patch: 14
+            w_patch: 14
+            embed_dim: 384
+            num_layers: 2
+            num_heads: 6
+            ffn_dim: 1536
+            dropout: 0.1
+        train_dinov2_multiscale_temporal_stride1:
+          max_frames: 16
+          batch_size: 16
+          num_workers: 4
+          learning_rate: 0.0001
+          weight_decay: 0.05
+          max_epochs: 30
+          early_stop_patience: 5
+          seed: 42
+          head_hidden_dim: 256
+          head_dropout: 0.1
+          use_cosine_warmup: true
+          warmup_frac: 0.05
+          img_size: 224
+          finetune: true
+          finetune_last_n_blocks: 1
+          backbone_lr: 1e-05
+          local_branch_section: local_branch_temporal_stride1
+    outs:
+    - path: data/08_reporting/dinov2_multiscale_temporal_stride1/train
+      hash: md5
+      md5: e9584a06e2a448aa725538a339bdd2f0.dir
+      size: 586194
+      nfiles: 6
+  evaluate_dinov2_multiscale_sweep@temporal_stride1-val:
+    cmd: uv run python scripts/evaluate.py --data-dir data/05_model_input/val 
+      --checkpoint 
+      data/06_models/dinov2_multiscale_temporal_stride1/best_checkpoint.pt 
+      --output-dir data/08_reporting/dinov2_multiscale_temporal_stride1/val 
+      --params-path params.yaml --params-key 
+      train_dinov2_multiscale_temporal_stride1
+    deps:
+    - path: data/05_model_input/val
+      hash: md5
+      md5: f8585e64f607a7a8d7d1651a9cbc930e.dir
+      size: 77725173
+      nfiles: 3219
+    - path: data/06_models/dinov2_multiscale_temporal_stride1
+      hash: md5
+      md5: af4d6248690c381f0d28469a6730fac3.dir
+      size: 278234097
+      nfiles: 5
+    - path: scripts/evaluate.py
+      hash: md5
+      md5: 44cba928007046cc57341989cd752879
+      size: 5231
+    - path: src/tube_multiscale_fusion/classifier.py
+      hash: md5
+      md5: 4a36dec854fd018990b5bee70bcffd9f
+      size: 3556
+    - path: src/tube_multiscale_fusion/dataset.py
+      hash: md5
+      md5: d7d3e37c27fd25f7612751b3c5869dd2
+      size: 3036
+    - path: src/tube_multiscale_fusion/eval_plots.py
+      hash: md5
+      md5: f76a38b124a936129d9e0cc6910b9e33
+      size: 3399
+    - path: src/tube_multiscale_fusion/fusion.py
+      hash: md5
+      md5: dce0c5c806436477dd7f1ff93bd261a5
+      size: 5179
+    - path: src/tube_multiscale_fusion/global_branch.py
+      hash: md5
+      md5: 4cae3af1b1a020daaeeae747bd05f966
+      size: 6341
+    - path: src/tube_multiscale_fusion/lit_module.py
+      hash: md5
+      md5: 4022aa3dc29c80e1a38a23182bec83b3
+      size: 7307
+    - path: src/tube_multiscale_fusion/local_branch.py
+      hash: md5
+      md5: 4a49a7286da503d7e0a21158d1c74295
+      size: 9228
+    params:
+      params.yaml:
+        fusion:
+          d_fusion: 384
+          num_layers: 2
+          num_heads: 6
+          ffn_dim: 1536
+          dropout: 0.1
+        global_branch:
+          backbone: vit_small_patch14_dinov2.lvd142m
+          aggregator:
+            num_layers: 2
+            num_heads: 6
+            ffn_dim: 1536
+            dropout: 0.1
+        local_branch_temporal_stride1:
+          grid_size: 2
+          cell_size: 112
+          tube_length: 4
+          temporal_stride: 1
+          encoder:
+            t_kernel: 2
+            h_patch: 14
+            w_patch: 14
+            embed_dim: 384
+            num_layers: 2
+            num_heads: 6
+            ffn_dim: 1536
+            dropout: 0.1
+        train_dinov2_multiscale_temporal_stride1:
+          max_frames: 16
+          batch_size: 16
+          num_workers: 4
+          learning_rate: 0.0001
+          weight_decay: 0.05
+          max_epochs: 30
+          early_stop_patience: 5
+          seed: 42
+          head_hidden_dim: 256
+          head_dropout: 0.1
+          use_cosine_warmup: true
+          warmup_frac: 0.05
+          img_size: 224
+          finetune: true
+          finetune_last_n_blocks: 1
+          backbone_lr: 1e-05
+          local_branch_section: local_branch_temporal_stride1
+    outs:
+    - path: data/08_reporting/dinov2_multiscale_temporal_stride1/val
+      hash: md5
+      md5: 23f1d5e1871d08c46830383d4ca8b280.dir
+      size: 148984
+      nfiles: 6
+  evaluate_dinov2_multiscale_sweep@temporal_len8-train:
+    cmd: uv run python scripts/evaluate.py --data-dir data/05_model_input/train 
+      --checkpoint 
+      data/06_models/dinov2_multiscale_temporal_len8/best_checkpoint.pt 
+      --output-dir data/08_reporting/dinov2_multiscale_temporal_len8/train 
+      --params-path params.yaml --params-key 
+      train_dinov2_multiscale_temporal_len8
+    deps:
+    - path: data/05_model_input/train
+      hash: md5
+      md5: 840f6c912a4c6ae3103627dd32594242.dir
+      size: 843667150
+      nfiles: 34039
+    - path: data/06_models/dinov2_multiscale_temporal_len8
+      hash: md5
+      md5: e8669b5629c79a9d377b199e1779e2c1.dir
+      size: 278384216
+      nfiles: 5
+    - path: scripts/evaluate.py
+      hash: md5
+      md5: 44cba928007046cc57341989cd752879
+      size: 5231
+    - path: src/tube_multiscale_fusion/classifier.py
+      hash: md5
+      md5: 4a36dec854fd018990b5bee70bcffd9f
+      size: 3556
+    - path: src/tube_multiscale_fusion/dataset.py
+      hash: md5
+      md5: d7d3e37c27fd25f7612751b3c5869dd2
+      size: 3036
+    - path: src/tube_multiscale_fusion/eval_plots.py
+      hash: md5
+      md5: f76a38b124a936129d9e0cc6910b9e33
+      size: 3399
+    - path: src/tube_multiscale_fusion/fusion.py
+      hash: md5
+      md5: dce0c5c806436477dd7f1ff93bd261a5
+      size: 5179
+    - path: src/tube_multiscale_fusion/global_branch.py
+      hash: md5
+      md5: 4cae3af1b1a020daaeeae747bd05f966
+      size: 6341
+    - path: src/tube_multiscale_fusion/lit_module.py
+      hash: md5
+      md5: 4022aa3dc29c80e1a38a23182bec83b3
+      size: 7307
+    - path: src/tube_multiscale_fusion/local_branch.py
+      hash: md5
+      md5: 4a49a7286da503d7e0a21158d1c74295
+      size: 9228
+    params:
+      params.yaml:
+        fusion:
+          d_fusion: 384
+          num_layers: 2
+          num_heads: 6
+          ffn_dim: 1536
+          dropout: 0.1
+        global_branch:
+          backbone: vit_small_patch14_dinov2.lvd142m
+          aggregator:
+            num_layers: 2
+            num_heads: 6
+            ffn_dim: 1536
+            dropout: 0.1
+        local_branch_temporal_len8:
+          grid_size: 2
+          cell_size: 112
+          tube_length: 8
+          temporal_stride: 4
+          encoder:
+            t_kernel: 2
+            h_patch: 14
+            w_patch: 14
+            embed_dim: 384
+            num_layers: 2
+            num_heads: 6
+            ffn_dim: 1536
+            dropout: 0.1
+        train_dinov2_multiscale_temporal_len8:
+          max_frames: 16
+          batch_size: 16
+          num_workers: 4
+          learning_rate: 0.0001
+          weight_decay: 0.05
+          max_epochs: 30
+          early_stop_patience: 5
+          seed: 42
+          head_hidden_dim: 256
+          head_dropout: 0.1
+          use_cosine_warmup: true
+          warmup_frac: 0.05
+          img_size: 224
+          finetune: true
+          finetune_last_n_blocks: 1
+          backbone_lr: 1e-05
+          local_branch_section: local_branch_temporal_len8
+    outs:
+    - path: data/08_reporting/dinov2_multiscale_temporal_len8/train
+      hash: md5
+      md5: dfad83dcb538e441275d86e54f46b912.dir
+      size: 586738
+      nfiles: 6
+  evaluate_dinov2_multiscale_sweep@temporal_len8-val:
+    cmd: uv run python scripts/evaluate.py --data-dir data/05_model_input/val 
+      --checkpoint 
+      data/06_models/dinov2_multiscale_temporal_len8/best_checkpoint.pt 
+      --output-dir data/08_reporting/dinov2_multiscale_temporal_len8/val 
+      --params-path params.yaml --params-key 
+      train_dinov2_multiscale_temporal_len8
+    deps:
+    - path: data/05_model_input/val
+      hash: md5
+      md5: f8585e64f607a7a8d7d1651a9cbc930e.dir
+      size: 77725173
+      nfiles: 3219
+    - path: data/06_models/dinov2_multiscale_temporal_len8
+      hash: md5
+      md5: e8669b5629c79a9d377b199e1779e2c1.dir
+      size: 278384216
+      nfiles: 5
+    - path: scripts/evaluate.py
+      hash: md5
+      md5: 44cba928007046cc57341989cd752879
+      size: 5231
+    - path: src/tube_multiscale_fusion/classifier.py
+      hash: md5
+      md5: 4a36dec854fd018990b5bee70bcffd9f
+      size: 3556
+    - path: src/tube_multiscale_fusion/dataset.py
+      hash: md5
+      md5: d7d3e37c27fd25f7612751b3c5869dd2
+      size: 3036
+    - path: src/tube_multiscale_fusion/eval_plots.py
+      hash: md5
+      md5: f76a38b124a936129d9e0cc6910b9e33
+      size: 3399
+    - path: src/tube_multiscale_fusion/fusion.py
+      hash: md5
+      md5: dce0c5c806436477dd7f1ff93bd261a5
+      size: 5179
+    - path: src/tube_multiscale_fusion/global_branch.py
+      hash: md5
+      md5: 4cae3af1b1a020daaeeae747bd05f966
+      size: 6341
+    - path: src/tube_multiscale_fusion/lit_module.py
+      hash: md5
+      md5: 4022aa3dc29c80e1a38a23182bec83b3
+      size: 7307
+    - path: src/tube_multiscale_fusion/local_branch.py
+      hash: md5
+      md5: 4a49a7286da503d7e0a21158d1c74295
+      size: 9228
+    params:
+      params.yaml:
+        fusion:
+          d_fusion: 384
+          num_layers: 2
+          num_heads: 6
+          ffn_dim: 1536
+          dropout: 0.1
+        global_branch:
+          backbone: vit_small_patch14_dinov2.lvd142m
+          aggregator:
+            num_layers: 2
+            num_heads: 6
+            ffn_dim: 1536
+            dropout: 0.1
+        local_branch_temporal_len8:
+          grid_size: 2
+          cell_size: 112
+          tube_length: 8
+          temporal_stride: 4
+          encoder:
+            t_kernel: 2
+            h_patch: 14
+            w_patch: 14
+            embed_dim: 384
+            num_layers: 2
+            num_heads: 6
+            ffn_dim: 1536
+            dropout: 0.1
+        train_dinov2_multiscale_temporal_len8:
+          max_frames: 16
+          batch_size: 16
+          num_workers: 4
+          learning_rate: 0.0001
+          weight_decay: 0.05
+          max_epochs: 30
+          early_stop_patience: 5
+          seed: 42
+          head_hidden_dim: 256
+          head_dropout: 0.1
+          use_cosine_warmup: true
+          warmup_frac: 0.05
+          img_size: 224
+          finetune: true
+          finetune_last_n_blocks: 1
+          backbone_lr: 1e-05
+          local_branch_section: local_branch_temporal_len8
+    outs:
+    - path: data/08_reporting/dinov2_multiscale_temporal_len8/val
+      hash: md5
+      md5: 79271c71ed3e07a1b73ad766daacb42d.dir
+      size: 148973
+      nfiles: 6
+  prepare:
+    cmd: uv run python scripts/prepare.py --model-repo 
+      pyronear/yolo11s_nimble-narwhal_v6.0.0 --model-filename best.pt 
+      --output-model-dir data/01_raw/models
+    deps:
+    - path: scripts/prepare.py
+      hash: md5
+      md5: bdccf5d50ba8f71ed560834ce23a3a52
+      size: 1778
+    params:
+      params.yaml:
+        prepare:
+          model_repo: pyronear/yolo11s_nimble-narwhal_v6.0.0
+          model_filename: best.pt
+    outs:
+    - path: data/01_raw/models
+      hash: md5
+      md5: aedaaff1a57ae80b0a7f66ad4e4c623b.dir
+      size: 19225626
+      nfiles: 1
+  train_dinov2_multiscale_sweep@spatial_4x4:
+    cmd: uv run python scripts/train.py --train-dir data/05_model_input/train 
+      --val-dir data/05_model_input/val --output-dir 
+      data/06_models/dinov2_multiscale_spatial_4x4 --params-path params.yaml 
+      --params-key train_dinov2_multiscale_spatial_4x4
+    deps:
+    - path: data/05_model_input/train
+      hash: md5
+      md5: 840f6c912a4c6ae3103627dd32594242.dir
+      size: 843667150
+      nfiles: 34039
+    - path: data/05_model_input/val
+      hash: md5
+      md5: f8585e64f607a7a8d7d1651a9cbc930e.dir
+      size: 77725173
+      nfiles: 3219
+    - path: scripts/train.py
+      hash: md5
+      md5: 2b58199a26cb37fa2f42fbf397bab6b2
+      size: 5441
+    - path: src/tube_multiscale_fusion/augment.py
+      hash: md5
+      md5: f52a42f92caa2fac859bf3a81a18971d
+      size: 9489
+    - path: src/tube_multiscale_fusion/classifier.py
+      hash: md5
+      md5: 4a36dec854fd018990b5bee70bcffd9f
+      size: 3556
+    - path: src/tube_multiscale_fusion/dataset.py
+      hash: md5
+      md5: d7d3e37c27fd25f7612751b3c5869dd2
+      size: 3036
+    - path: src/tube_multiscale_fusion/fusion.py
+      hash: md5
+      md5: dce0c5c806436477dd7f1ff93bd261a5
+      size: 5179
+    - path: src/tube_multiscale_fusion/global_branch.py
+      hash: md5
+      md5: 4cae3af1b1a020daaeeae747bd05f966
+      size: 6341
+    - path: src/tube_multiscale_fusion/lit_module.py
+      hash: md5
+      md5: 4022aa3dc29c80e1a38a23182bec83b3
+      size: 7307
+    - path: src/tube_multiscale_fusion/local_branch.py
+      hash: md5
+      md5: 4a49a7286da503d7e0a21158d1c74295
+      size: 9228
+    params:
+      params.yaml:
+        augment:
+          enabled: true
+          spatial:
+            flip_prob: 0.5
+            rotation_deg: 5.0
+            scale_range:
+            - 0.9
+            - 1.1
+            translate_frac: 0.05
+          photometric:
+            brightness_range:
+            - 0.8
+            - 1.2
+            contrast_range:
+            - 0.8
+            - 1.2
+            saturation_range:
+            - 0.8
+            - 1.2
+          temporal:
+            subseq_prob: 0.5
+            subseq_min_len: 4
+            stride_prob: 0.25
+            frame_drop_prob: 0.15
+            min_valid_after_drop: 4
+        fusion:
+          d_fusion: 384
+          num_layers: 2
+          num_heads: 6
+          ffn_dim: 1536
+          dropout: 0.1
+        global_branch:
+          backbone: vit_small_patch14_dinov2.lvd142m
+          aggregator:
+            num_layers: 2
+            num_heads: 6
+            ffn_dim: 1536
+            dropout: 0.1
+        local_branch_spatial_4x4:
+          grid_size: 4
+          cell_size: 56
+          tube_length: 4
+          temporal_stride: 2
+          encoder:
+            t_kernel: 2
+            h_patch: 14
+            w_patch: 14
+            embed_dim: 384
+            num_layers: 2
+            num_heads: 6
+            ffn_dim: 1536
+            dropout: 0.1
+        train_dinov2_multiscale_spatial_4x4:
+          max_frames: 16
+          batch_size: 16
+          num_workers: 4
+          learning_rate: 0.0001
+          weight_decay: 0.05
+          max_epochs: 30
+          early_stop_patience: 5
+          seed: 42
+          head_hidden_dim: 256
+          head_dropout: 0.1
+          use_cosine_warmup: true
+          warmup_frac: 0.05
+          img_size: 224
+          finetune: true
+          finetune_last_n_blocks: 1
+          backbone_lr: 1e-05
+          local_branch_section: local_branch_spatial_4x4
+    outs:
+    - path: data/06_models/dinov2_multiscale_spatial_4x4
+      hash: md5
+      md5: 7986079d3746bde09cd8c024cf5c2a27.dir
+      size: 278212941
+      nfiles: 5
+  evaluate_dinov2_multiscale_sweep@spatial_4x4-train:
+    cmd: uv run python scripts/evaluate.py --data-dir data/05_model_input/train 
+      --checkpoint 
+      data/06_models/dinov2_multiscale_spatial_4x4/best_checkpoint.pt 
+      --output-dir data/08_reporting/dinov2_multiscale_spatial_4x4/train 
+      --params-path params.yaml --params-key train_dinov2_multiscale_spatial_4x4
+    deps:
+    - path: data/05_model_input/train
+      hash: md5
+      md5: 840f6c912a4c6ae3103627dd32594242.dir
+      size: 843667150
+      nfiles: 34039
+    - path: data/06_models/dinov2_multiscale_spatial_4x4
+      hash: md5
+      md5: 7986079d3746bde09cd8c024cf5c2a27.dir
+      size: 278212941
+      nfiles: 5
+    - path: scripts/evaluate.py
+      hash: md5
+      md5: 44cba928007046cc57341989cd752879
+      size: 5231
+    - path: src/tube_multiscale_fusion/classifier.py
+      hash: md5
+      md5: 4a36dec854fd018990b5bee70bcffd9f
+      size: 3556
+    - path: src/tube_multiscale_fusion/dataset.py
+      hash: md5
+      md5: d7d3e37c27fd25f7612751b3c5869dd2
+      size: 3036
+    - path: src/tube_multiscale_fusion/eval_plots.py
+      hash: md5
+      md5: f76a38b124a936129d9e0cc6910b9e33
+      size: 3399
+    - path: src/tube_multiscale_fusion/fusion.py
+      hash: md5
+      md5: dce0c5c806436477dd7f1ff93bd261a5
+      size: 5179
+    - path: src/tube_multiscale_fusion/global_branch.py
+      hash: md5
+      md5: 4cae3af1b1a020daaeeae747bd05f966
+      size: 6341
+    - path: src/tube_multiscale_fusion/lit_module.py
+      hash: md5
+      md5: 4022aa3dc29c80e1a38a23182bec83b3
+      size: 7307
+    - path: src/tube_multiscale_fusion/local_branch.py
+      hash: md5
+      md5: 4a49a7286da503d7e0a21158d1c74295
+      size: 9228
+    params:
+      params.yaml:
+        fusion:
+          d_fusion: 384
+          num_layers: 2
+          num_heads: 6
+          ffn_dim: 1536
+          dropout: 0.1
+        global_branch:
+          backbone: vit_small_patch14_dinov2.lvd142m
+          aggregator:
+            num_layers: 2
+            num_heads: 6
+            ffn_dim: 1536
+            dropout: 0.1
+        local_branch_spatial_4x4:
+          grid_size: 4
+          cell_size: 56
+          tube_length: 4
+          temporal_stride: 2
+          encoder:
+            t_kernel: 2
+            h_patch: 14
+            w_patch: 14
+            embed_dim: 384
+            num_layers: 2
+            num_heads: 6
+            ffn_dim: 1536
+            dropout: 0.1
+        train_dinov2_multiscale_spatial_4x4:
+          max_frames: 16
+          batch_size: 16
+          num_workers: 4
+          learning_rate: 0.0001
+          weight_decay: 0.05
+          max_epochs: 30
+          early_stop_patience: 5
+          seed: 42
+          head_hidden_dim: 256
+          head_dropout: 0.1
+          use_cosine_warmup: true
+          warmup_frac: 0.05
+          img_size: 224
+          finetune: true
+          finetune_last_n_blocks: 1
+          backbone_lr: 1e-05
+          local_branch_section: local_branch_spatial_4x4
+    outs:
+    - path: data/08_reporting/dinov2_multiscale_spatial_4x4/train
+      hash: md5
+      md5: 3a9b78c9e4f5882eb1d516653f10592b.dir
+      size: 582686
+      nfiles: 6
+  evaluate_dinov2_multiscale_sweep@spatial_4x4-val:
+    cmd: uv run python scripts/evaluate.py --data-dir data/05_model_input/val 
+      --checkpoint 
+      data/06_models/dinov2_multiscale_spatial_4x4/best_checkpoint.pt 
+      --output-dir data/08_reporting/dinov2_multiscale_spatial_4x4/val 
+      --params-path params.yaml --params-key train_dinov2_multiscale_spatial_4x4
+    deps:
+    - path: data/05_model_input/val
+      hash: md5
+      md5: f8585e64f607a7a8d7d1651a9cbc930e.dir
+      size: 77725173
+      nfiles: 3219
+    - path: data/06_models/dinov2_multiscale_spatial_4x4
+      hash: md5
+      md5: 7986079d3746bde09cd8c024cf5c2a27.dir
+      size: 278212941
+      nfiles: 5
+    - path: scripts/evaluate.py
+      hash: md5
+      md5: 44cba928007046cc57341989cd752879
+      size: 5231
+    - path: src/tube_multiscale_fusion/classifier.py
+      hash: md5
+      md5: 4a36dec854fd018990b5bee70bcffd9f
+      size: 3556
+    - path: src/tube_multiscale_fusion/dataset.py
+      hash: md5
+      md5: d7d3e37c27fd25f7612751b3c5869dd2
+      size: 3036
+    - path: src/tube_multiscale_fusion/eval_plots.py
+      hash: md5
+      md5: f76a38b124a936129d9e0cc6910b9e33
+      size: 3399
+    - path: src/tube_multiscale_fusion/fusion.py
+      hash: md5
+      md5: dce0c5c806436477dd7f1ff93bd261a5
+      size: 5179
+    - path: src/tube_multiscale_fusion/global_branch.py
+      hash: md5
+      md5: 4cae3af1b1a020daaeeae747bd05f966
+      size: 6341
+    - path: src/tube_multiscale_fusion/lit_module.py
+      hash: md5
+      md5: 4022aa3dc29c80e1a38a23182bec83b3
+      size: 7307
+    - path: src/tube_multiscale_fusion/local_branch.py
+      hash: md5
+      md5: 4a49a7286da503d7e0a21158d1c74295
+      size: 9228
+    params:
+      params.yaml:
+        fusion:
+          d_fusion: 384
+          num_layers: 2
+          num_heads: 6
+          ffn_dim: 1536
+          dropout: 0.1
+        global_branch:
+          backbone: vit_small_patch14_dinov2.lvd142m
+          aggregator:
+            num_layers: 2
+            num_heads: 6
+            ffn_dim: 1536
+            dropout: 0.1
+        local_branch_spatial_4x4:
+          grid_size: 4
+          cell_size: 56
+          tube_length: 4
+          temporal_stride: 2
+          encoder:
+            t_kernel: 2
+            h_patch: 14
+            w_patch: 14
+            embed_dim: 384
+            num_layers: 2
+            num_heads: 6
+            ffn_dim: 1536
+            dropout: 0.1
+        train_dinov2_multiscale_spatial_4x4:
+          max_frames: 16
+          batch_size: 16
+          num_workers: 4
+          learning_rate: 0.0001
+          weight_decay: 0.05
+          max_epochs: 30
+          early_stop_patience: 5
+          seed: 42
+          head_hidden_dim: 256
+          head_dropout: 0.1
+          use_cosine_warmup: true
+          warmup_frac: 0.05
+          img_size: 224
+          finetune: true
+          finetune_last_n_blocks: 1
+          backbone_lr: 1e-05
+          local_branch_section: local_branch_spatial_4x4
+    outs:
+    - path: data/08_reporting/dinov2_multiscale_spatial_4x4/val
+      hash: md5
+      md5: af2b0aa9895ae86bae2b98b0905d002f.dir
+      size: 143450
+      nfiles: 6
+  package_dinov2_multiscale:
+    cmd: uv run python scripts/package_model.py --checkpoint 
+      data/06_models/dinov2_multiscale/best_checkpoint.pt --params-path 
+      params.yaml --params-key train_dinov2_multiscale --output 
+      data/07_model_output/dinov2_multiscale/model_package.zip
+    deps:
+    - path: data/06_models/dinov2_multiscale
+      hash: md5
+      md5: e53145752327bb1e91450d552d57382f.dir
+      size: 278684379
+      nfiles: 5
+    - path: scripts/package_model.py
+      hash: md5
+      md5: 7d2524bd2f9813be73b53ace822eb32d
+      size: 4652
+    params:
+      params.yaml:
+        fusion:
+          d_fusion: 384
+          num_layers: 2
+          num_heads: 6
+          ffn_dim: 1536
+          dropout: 0.1
+        global_branch:
+          backbone: vit_small_patch14_dinov2.lvd142m
+          aggregator:
+            num_layers: 2
+            num_heads: 6
+            ffn_dim: 1536
+            dropout: 0.1
+        local_branch:
+          grid_size: 2
+          cell_size: 112
+          tube_length: 4
+          temporal_stride: 2
+          encoder:
+            t_kernel: 2
+            h_patch: 14
+            w_patch: 14
+            embed_dim: 384
+            num_layers: 2
+            num_heads: 6
+            ffn_dim: 1536
+            dropout: 0.1
+        train_dinov2_multiscale:
+          max_frames: 16
+          batch_size: 16
+          num_workers: 4
+          learning_rate: 0.0001
+          weight_decay: 0.05
+          max_epochs: 30
+          early_stop_patience: 5
+          seed: 42
+          head_hidden_dim: 256
+          head_dropout: 0.1
+          use_cosine_warmup: true
+          warmup_frac: 0.05
+          img_size: 224
+          finetune: true
+          finetune_last_n_blocks: 1
+          backbone_lr: 1e-05
+    outs:
+    - path: data/07_model_output/dinov2_multiscale/model_package.zip
+      hash: md5
+      md5: 2cd9dd674b0b328f1d417ecefa1f2d68
+      size: 256730022
+  benchmark_dinov2_multiscale:
+    cmd: uv run python scripts/benchmark.py --data-dir data/05_model_input/val 
+      --checkpoint data/06_models/dinov2_multiscale/best_checkpoint.pt 
+      --predictions data/08_reporting/dinov2_multiscale/val/predictions.json 
+      --output data/08_reporting/dinov2_multiscale_benchmark/benchmark.json
+    deps:
+    - path: data/05_model_input/val
+      hash: md5
+      md5: f8585e64f607a7a8d7d1651a9cbc930e.dir
+      size: 77725173
+      nfiles: 3219
+    - path: data/06_models/dinov2_multiscale
+      hash: md5
+      md5: e53145752327bb1e91450d552d57382f.dir
+      size: 278684379
+      nfiles: 5
+    - path: data/08_reporting/dinov2_multiscale/val
+      hash: md5
+      md5: cbbb27b14880e62866f2ce722b5e31b6.dir
+      size: 149127
+      nfiles: 6
+    - path: scripts/benchmark.py
+      hash: md5
+      md5: d8f0f237f2699b03ca9b0b455a2cdf5d
+      size: 8458
+    - path: src/tube_multiscale_fusion/classifier.py
+      hash: md5
+      md5: 4a36dec854fd018990b5bee70bcffd9f
+      size: 3556
+    - path: src/tube_multiscale_fusion/dataset.py
+      hash: md5
+      md5: d7d3e37c27fd25f7612751b3c5869dd2
+      size: 3036
+    - path: src/tube_multiscale_fusion/fusion.py
+      hash: md5
+      md5: dce0c5c806436477dd7f1ff93bd261a5
+      size: 5179
+    - path: src/tube_multiscale_fusion/global_branch.py
+      hash: md5
+      md5: 4cae3af1b1a020daaeeae747bd05f966
+      size: 6341
+    - path: src/tube_multiscale_fusion/lit_module.py
+      hash: md5
+      md5: 4022aa3dc29c80e1a38a23182bec83b3
+      size: 7307
+    - path: src/tube_multiscale_fusion/local_branch.py
+      hash: md5
+      md5: 4a49a7286da503d7e0a21158d1c74295
+      size: 9228
+    params:
+      params.yaml:
+        fusion:
+          d_fusion: 384
+          num_layers: 2
+          num_heads: 6
+          ffn_dim: 1536
+          dropout: 0.1
+        global_branch:
+          backbone: vit_small_patch14_dinov2.lvd142m
+          aggregator:
+            num_layers: 2
+            num_heads: 6
+            ffn_dim: 1536
+            dropout: 0.1
+        local_branch:
+          grid_size: 2
+          cell_size: 112
+          tube_length: 4
+          temporal_stride: 2
+          encoder:
+            t_kernel: 2
+            h_patch: 14
+            w_patch: 14
+            embed_dim: 384
+            num_layers: 2
+            num_heads: 6
+            ffn_dim: 1536
+            dropout: 0.1
+        train_dinov2_multiscale:
+          max_frames: 16
+          batch_size: 16
+          num_workers: 4
+          learning_rate: 0.0001
+          weight_decay: 0.05
+          max_epochs: 30
+          early_stop_patience: 5
+          seed: 42
+          head_hidden_dim: 256
+          head_dropout: 0.1
+          use_cosine_warmup: true
+          warmup_frac: 0.05
+          img_size: 224
+          finetune: true
+          finetune_last_n_blocks: 1
+          backbone_lr: 1e-05
+    outs:
+    - path: data/08_reporting/dinov2_multiscale_benchmark/benchmark.json
+      hash: md5
+      md5: 7c66931ea3aa26855736829baf1a22a6
+      size: 1134
+  train_ablation_no_temporal:
+    cmd: uv run python scripts/train_ablation.py --train-dir 
+      data/05_model_input/train --val-dir data/05_model_input/val --output-dir 
+      data/06_models/ablation_no_temporal --params-path params.yaml --params-key
+      train_ablation_no_temporal
+    deps:
+    - path: data/05_model_input/train
+      hash: md5
+      md5: 840f6c912a4c6ae3103627dd32594242.dir
+      size: 843667150
+      nfiles: 34039
+    - path: data/05_model_input/val
+      hash: md5
+      md5: f8585e64f607a7a8d7d1651a9cbc930e.dir
+      size: 77725173
+      nfiles: 3219
+    - path: scripts/train_ablation.py
+      hash: md5
+      md5: 107959dd0d319e3d5803df4c4aac7cb6
+      size: 4789
+    - path: src/tube_multiscale_fusion/ablation_classifier.py
+      hash: md5
+      md5: ab66c9e55c1e3a3942f1537fb82134ed
+      size: 3641
+    - path: src/tube_multiscale_fusion/augment.py
+      hash: md5
+      md5: f52a42f92caa2fac859bf3a81a18971d
+      size: 9489
+    - path: src/tube_multiscale_fusion/dataset.py
+      hash: md5
+      md5: d7d3e37c27fd25f7612751b3c5869dd2
+      size: 3036
+    - path: src/tube_multiscale_fusion/fusion.py
+      hash: md5
+      md5: dce0c5c806436477dd7f1ff93bd261a5
+      size: 5179
+    - path: src/tube_multiscale_fusion/lit_ablation.py
+      hash: md5
+      md5: aa5e159aa8566735091ad22acdc6263d
+      size: 5212
+    - path: src/tube_multiscale_fusion/local_branch.py
+      hash: md5
+      md5: 4a49a7286da503d7e0a21158d1c74295
+      size: 9228
+    params:
+      params.yaml:
+        augment:
+          enabled: true
+          spatial:
+            flip_prob: 0.5
+            rotation_deg: 5.0
+            scale_range:
+            - 0.9
+            - 1.1
+            translate_frac: 0.05
+          photometric:
+            brightness_range:
+            - 0.8
+            - 1.2
+            contrast_range:
+            - 0.8
+            - 1.2
+            saturation_range:
+            - 0.8
+            - 1.2
+          temporal:
+            subseq_prob: 0.5
+            subseq_min_len: 4
+            stride_prob: 0.25
+            frame_drop_prob: 0.15
+            min_valid_after_drop: 4
+        fusion:
+          d_fusion: 384
+          num_layers: 2
+          num_heads: 6
+          ffn_dim: 1536
+          dropout: 0.1
+        local_branch:
+          grid_size: 2
+          cell_size: 112
+          tube_length: 4
+          temporal_stride: 2
+          encoder:
+            t_kernel: 2
+            h_patch: 14
+            w_patch: 14
+            embed_dim: 384
+            num_layers: 2
+            num_heads: 6
+            ffn_dim: 1536
+            dropout: 0.1
+        train_ablation_no_temporal:
+          max_frames: 16
+          batch_size: 16
+          num_workers: 4
+          learning_rate: 0.0001
+          weight_decay: 0.05
+          max_epochs: 30
+          early_stop_patience: 5
+          seed: 42
+          head_hidden_dim: 256
+          head_dropout: 0.1
+          use_cosine_warmup: true
+          warmup_frac: 0.05
+          img_size: 224
+          local_branch_section: local_branch
+          query_dim: 384
+    outs:
+    - path: data/06_models/ablation_no_temporal
+      hash: md5
+      md5: 24aae56b6e78bebe658a57803aca99d5.dir
+      size: 135163469
+      nfiles: 5
+  evaluate_ablation_no_temporal@val:
+    cmd: uv run python scripts/evaluate_ablation.py --data-dir 
+      data/05_model_input/val --checkpoint 
+      data/06_models/ablation_no_temporal/best_checkpoint.pt --output-dir 
+      data/08_reporting/ablation_no_temporal/val --params-path params.yaml 
+      --params-key train_ablation_no_temporal
+    deps:
+    - path: data/05_model_input/val
+      hash: md5
+      md5: f8585e64f607a7a8d7d1651a9cbc930e.dir
+      size: 77725173
+      nfiles: 3219
+    - path: data/06_models/ablation_no_temporal
+      hash: md5
+      md5: 24aae56b6e78bebe658a57803aca99d5.dir
+      size: 135163469
+      nfiles: 5
+    - path: scripts/evaluate_ablation.py
+      hash: md5
+      md5: c3c9a3d0195471eb287ebd4a21b4321e
+      size: 5231
+    - path: src/tube_multiscale_fusion/ablation_classifier.py
+      hash: md5
+      md5: ab66c9e55c1e3a3942f1537fb82134ed
+      size: 3641
+    - path: src/tube_multiscale_fusion/dataset.py
+      hash: md5
+      md5: d7d3e37c27fd25f7612751b3c5869dd2
+      size: 3036
+    - path: src/tube_multiscale_fusion/eval_plots.py
+      hash: md5
+      md5: f76a38b124a936129d9e0cc6910b9e33
+      size: 3399
+    - path: src/tube_multiscale_fusion/fusion.py
+      hash: md5
+      md5: dce0c5c806436477dd7f1ff93bd261a5
+      size: 5179
+    - path: src/tube_multiscale_fusion/lit_ablation.py
+      hash: md5
+      md5: aa5e159aa8566735091ad22acdc6263d
+      size: 5212
+    - path: src/tube_multiscale_fusion/local_branch.py
+      hash: md5
+      md5: 4a49a7286da503d7e0a21158d1c74295
+      size: 9228
+    params:
+      params.yaml:
+        fusion:
+          d_fusion: 384
+          num_layers: 2
+          num_heads: 6
+          ffn_dim: 1536
+          dropout: 0.1
+        local_branch:
+          grid_size: 2
+          cell_size: 112
+          tube_length: 4
+          temporal_stride: 2
+          encoder:
+            t_kernel: 2
+            h_patch: 14
+            w_patch: 14
+            embed_dim: 384
+            num_layers: 2
+            num_heads: 6
+            ffn_dim: 1536
+            dropout: 0.1
+        train_ablation_no_temporal:
+          max_frames: 16
+          batch_size: 16
+          num_workers: 4
+          learning_rate: 0.0001
+          weight_decay: 0.05
+          max_epochs: 30
+          early_stop_patience: 5
+          seed: 42
+          head_hidden_dim: 256
+          head_dropout: 0.1
+          use_cosine_warmup: true
+          warmup_frac: 0.05
+          img_size: 224
+          local_branch_section: local_branch
+          query_dim: 384
+    outs:
+    - path: data/08_reporting/ablation_no_temporal/val
+      hash: md5
+      md5: 9ae31aeebf94c0701647092d9f577ed6.dir
+      size: 149002
+      nfiles: 6
+  evaluate_ablation_no_temporal@train:
+    cmd: uv run python scripts/evaluate_ablation.py --data-dir 
+      data/05_model_input/train --checkpoint 
+      data/06_models/ablation_no_temporal/best_checkpoint.pt --output-dir 
+      data/08_reporting/ablation_no_temporal/train --params-path params.yaml 
+      --params-key train_ablation_no_temporal
+    deps:
+    - path: data/05_model_input/train
+      hash: md5
+      md5: 840f6c912a4c6ae3103627dd32594242.dir
+      size: 843667150
+      nfiles: 34039
+    - path: data/06_models/ablation_no_temporal
+      hash: md5
+      md5: 24aae56b6e78bebe658a57803aca99d5.dir
+      size: 135163469
+      nfiles: 5
+    - path: scripts/evaluate_ablation.py
+      hash: md5
+      md5: c3c9a3d0195471eb287ebd4a21b4321e
+      size: 5231
+    - path: src/tube_multiscale_fusion/ablation_classifier.py
+      hash: md5
+      md5: ab66c9e55c1e3a3942f1537fb82134ed
+      size: 3641
+    - path: src/tube_multiscale_fusion/dataset.py
+      hash: md5
+      md5: d7d3e37c27fd25f7612751b3c5869dd2
+      size: 3036
+    - path: src/tube_multiscale_fusion/eval_plots.py
+      hash: md5
+      md5: f76a38b124a936129d9e0cc6910b9e33
+      size: 3399
+    - path: src/tube_multiscale_fusion/fusion.py
+      hash: md5
+      md5: dce0c5c806436477dd7f1ff93bd261a5
+      size: 5179
+    - path: src/tube_multiscale_fusion/lit_ablation.py
+      hash: md5
+      md5: aa5e159aa8566735091ad22acdc6263d
+      size: 5212
+    - path: src/tube_multiscale_fusion/local_branch.py
+      hash: md5
+      md5: 4a49a7286da503d7e0a21158d1c74295
+      size: 9228
+    params:
+      params.yaml:
+        fusion:
+          d_fusion: 384
+          num_layers: 2
+          num_heads: 6
+          ffn_dim: 1536
+          dropout: 0.1
+        local_branch:
+          grid_size: 2
+          cell_size: 112
+          tube_length: 4
+          temporal_stride: 2
+          encoder:
+            t_kernel: 2
+            h_patch: 14
+            w_patch: 14
+            embed_dim: 384
+            num_layers: 2
+            num_heads: 6
+            ffn_dim: 1536
+            dropout: 0.1
+        train_ablation_no_temporal:
+          max_frames: 16
+          batch_size: 16
+          num_workers: 4
+          learning_rate: 0.0001
+          weight_decay: 0.05
+          max_epochs: 30
+          early_stop_patience: 5
+          seed: 42
+          head_hidden_dim: 256
+          head_dropout: 0.1
+          use_cosine_warmup: true
+          warmup_frac: 0.05
+          img_size: 224
+          local_branch_section: local_branch
+          query_dim: 384
+    outs:
+    - path: data/08_reporting/ablation_no_temporal/train
+      hash: md5
+      md5: 8d5719e78491a75c82ecb030950f773d.dir
+      size: 590704
+      nfiles: 6
+  train_ablation_weighted_mean:
+    cmd: uv run python scripts/train_ablation_global.py --train-dir 
+      data/05_model_input/train --val-dir data/05_model_input/val --output-dir 
+      data/06_models/ablation_weighted_mean --params-path params.yaml 
+      --params-key train_ablation_weighted_mean
+    deps:
+    - path: data/05_model_input/train
+      hash: md5
+      md5: 840f6c912a4c6ae3103627dd32594242.dir
+      size: 843667150
+      nfiles: 34039
+    - path: data/05_model_input/val
+      hash: md5
+      md5: f8585e64f607a7a8d7d1651a9cbc930e.dir
+      size: 77725173
+      nfiles: 3219
+    - path: scripts/train_ablation_global.py
+      hash: md5
+      md5: 6c5570241d121de71363ba856e5e0105
+      size: 5234
+    - path: src/tube_multiscale_fusion/ablation_classifier.py
+      hash: md5
+      md5: 95bc858298ff235b637f7e039875f083
+      size: 8610
+    - path: src/tube_multiscale_fusion/augment.py
+      hash: md5
+      md5: f52a42f92caa2fac859bf3a81a18971d
+      size: 9489
+    - path: src/tube_multiscale_fusion/dataset.py
+      hash: md5
+      md5: d7d3e37c27fd25f7612751b3c5869dd2
+      size: 3036
+    - path: src/tube_multiscale_fusion/fusion.py
+      hash: md5
+      md5: 695dd2af05cb3a47cc3387c9c4ba210d
+      size: 7308
+    - path: src/tube_multiscale_fusion/global_branch.py
+      hash: md5
+      md5: 4cae3af1b1a020daaeeae747bd05f966
+      size: 6341
+    - path: src/tube_multiscale_fusion/lit_ablation.py
+      hash: md5
+      md5: e4608c6fa3f03dd1bc48b573602d29eb
+      size: 13172
+    - path: src/tube_multiscale_fusion/local_branch.py
+      hash: md5
+      md5: 4a49a7286da503d7e0a21158d1c74295
+      size: 9228
+    params:
+      params.yaml:
+        augment:
+          enabled: true
+          spatial:
+            flip_prob: 0.5
+            rotation_deg: 5.0
+            scale_range:
+            - 0.9
+            - 1.1
+            translate_frac: 0.05
+          photometric:
+            brightness_range:
+            - 0.8
+            - 1.2
+            contrast_range:
+            - 0.8
+            - 1.2
+            saturation_range:
+            - 0.8
+            - 1.2
+          temporal:
+            subseq_prob: 0.5
+            subseq_min_len: 4
+            stride_prob: 0.25
+            frame_drop_prob: 0.15
+            min_valid_after_drop: 4
+        fusion:
+          d_fusion: 384
+          num_layers: 2
+          num_heads: 6
+          ffn_dim: 1536
+          dropout: 0.1
+        global_branch:
+          backbone: vit_small_patch14_dinov2.lvd142m
+          aggregator:
+            num_layers: 2
+            num_heads: 6
+            ffn_dim: 1536
+            dropout: 0.1
+        local_branch:
+          grid_size: 2
+          cell_size: 112
+          tube_length: 4
+          temporal_stride: 2
+          encoder:
+            t_kernel: 2
+            h_patch: 14
+            w_patch: 14
+            embed_dim: 384
+            num_layers: 2
+            num_heads: 6
+            ffn_dim: 1536
+            dropout: 0.1
+        train_ablation_weighted_mean:
+          max_frames: 16
+          batch_size: 16
+          num_workers: 4
+          learning_rate: 0.0001
+          weight_decay: 0.05
+          max_epochs: 30
+          early_stop_patience: 5
+          seed: 42
+          head_hidden_dim: 256
+          head_dropout: 0.1
+          use_cosine_warmup: true
+          warmup_frac: 0.05
+          img_size: 224
+          ablation_variant: weighted_mean
+          finetune: true
+          finetune_last_n_blocks: 1
+          backbone_lr: 1e-05
+    outs:
+    - path: data/06_models/ablation_weighted_mean
+      hash: md5
+      md5: b27b6ac456b6b49c9f2c177b4a2100ae.dir
+      size: 193430824
+      nfiles: 5
+  evaluate_ablation_weighted_mean@val:
+    cmd: uv run python scripts/evaluate_ablation_global.py --data-dir 
+      data/05_model_input/val --checkpoint 
+      data/06_models/ablation_weighted_mean/best_checkpoint.pt --output-dir 
+      data/08_reporting/ablation_weighted_mean/val --params-path params.yaml 
+      --params-key train_ablation_weighted_mean
+    deps:
+    - path: data/05_model_input/val
+      hash: md5
+      md5: f8585e64f607a7a8d7d1651a9cbc930e.dir
+      size: 77725173
+      nfiles: 3219
+    - path: data/06_models/ablation_weighted_mean
+      hash: md5
+      md5: b27b6ac456b6b49c9f2c177b4a2100ae.dir
+      size: 193430824
+      nfiles: 5
+    - path: scripts/evaluate_ablation_global.py
+      hash: md5
+      md5: 6d3be966e0bf9143aa20ba3228a33c67
+      size: 5251
+    - path: src/tube_multiscale_fusion/ablation_classifier.py
+      hash: md5
+      md5: 95bc858298ff235b637f7e039875f083
+      size: 8610
+    - path: src/tube_multiscale_fusion/dataset.py
+      hash: md5
+      md5: d7d3e37c27fd25f7612751b3c5869dd2
+      size: 3036
+    - path: src/tube_multiscale_fusion/eval_plots.py
+      hash: md5
+      md5: f76a38b124a936129d9e0cc6910b9e33
+      size: 3399
+    - path: src/tube_multiscale_fusion/fusion.py
+      hash: md5
+      md5: 695dd2af05cb3a47cc3387c9c4ba210d
+      size: 7308
+    - path: src/tube_multiscale_fusion/global_branch.py
+      hash: md5
+      md5: 4cae3af1b1a020daaeeae747bd05f966
+      size: 6341
+    - path: src/tube_multiscale_fusion/lit_ablation.py
+      hash: md5
+      md5: e4608c6fa3f03dd1bc48b573602d29eb
+      size: 13172
+    - path: src/tube_multiscale_fusion/local_branch.py
+      hash: md5
+      md5: 4a49a7286da503d7e0a21158d1c74295
+      size: 9228
+    params:
+      params.yaml:
+        fusion:
+          d_fusion: 384
+          num_layers: 2
+          num_heads: 6
+          ffn_dim: 1536
+          dropout: 0.1
+        global_branch:
+          backbone: vit_small_patch14_dinov2.lvd142m
+          aggregator:
+            num_layers: 2
+            num_heads: 6
+            ffn_dim: 1536
+            dropout: 0.1
+        local_branch:
+          grid_size: 2
+          cell_size: 112
+          tube_length: 4
+          temporal_stride: 2
+          encoder:
+            t_kernel: 2
+            h_patch: 14
+            w_patch: 14
+            embed_dim: 384
+            num_layers: 2
+            num_heads: 6
+            ffn_dim: 1536
+            dropout: 0.1
+        train_ablation_weighted_mean:
+          max_frames: 16
+          batch_size: 16
+          num_workers: 4
+          learning_rate: 0.0001
+          weight_decay: 0.05
+          max_epochs: 30
+          early_stop_patience: 5
+          seed: 42
+          head_hidden_dim: 256
+          head_dropout: 0.1
+          use_cosine_warmup: true
+          warmup_frac: 0.05
+          img_size: 224
+          ablation_variant: weighted_mean
+          finetune: true
+          finetune_last_n_blocks: 1
+          backbone_lr: 1e-05
+    outs:
+    - path: data/08_reporting/ablation_weighted_mean/val
+      hash: md5
+      md5: 60b321c084d7df4d033a8c27f03dbfca.dir
+      size: 145469
+      nfiles: 6
+  train_ablation_no_spatial:
+    cmd: uv run python scripts/train_ablation_global.py --train-dir 
+      data/05_model_input/train --val-dir data/05_model_input/val --output-dir 
+      data/06_models/ablation_no_spatial --params-path params.yaml --params-key 
+      train_ablation_no_spatial
+    deps:
+    - path: data/05_model_input/train
+      hash: md5
+      md5: 840f6c912a4c6ae3103627dd32594242.dir
+      size: 843667150
+      nfiles: 34039
+    - path: data/05_model_input/val
+      hash: md5
+      md5: f8585e64f607a7a8d7d1651a9cbc930e.dir
+      size: 77725173
+      nfiles: 3219
+    - path: scripts/train_ablation_global.py
+      hash: md5
+      md5: 6c5570241d121de71363ba856e5e0105
+      size: 5234
+    - path: src/tube_multiscale_fusion/ablation_classifier.py
+      hash: md5
+      md5: 95bc858298ff235b637f7e039875f083
+      size: 8610
+    - path: src/tube_multiscale_fusion/augment.py
+      hash: md5
+      md5: f52a42f92caa2fac859bf3a81a18971d
+      size: 9489
+    - path: src/tube_multiscale_fusion/dataset.py
+      hash: md5
+      md5: d7d3e37c27fd25f7612751b3c5869dd2
+      size: 3036
+    - path: src/tube_multiscale_fusion/global_branch.py
+      hash: md5
+      md5: 4cae3af1b1a020daaeeae747bd05f966
+      size: 6341
+    - path: src/tube_multiscale_fusion/lit_ablation.py
+      hash: md5
+      md5: e4608c6fa3f03dd1bc48b573602d29eb
+      size: 13172
+    params:
+      params.yaml:
+        augment:
+          enabled: true
+          spatial:
+            flip_prob: 0.5
+            rotation_deg: 5.0
+            scale_range:
+            - 0.9
+            - 1.1
+            translate_frac: 0.05
+          photometric:
+            brightness_range:
+            - 0.8
+            - 1.2
+            contrast_range:
+            - 0.8
+            - 1.2
+            saturation_range:
+            - 0.8
+            - 1.2
+          temporal:
+            subseq_prob: 0.5
+            subseq_min_len: 4
+            stride_prob: 0.25
+            frame_drop_prob: 0.15
+            min_valid_after_drop: 4
+        global_branch:
+          backbone: vit_small_patch14_dinov2.lvd142m
+          aggregator:
+            num_layers: 2
+            num_heads: 6
+            ffn_dim: 1536
+            dropout: 0.1
+        train_ablation_no_spatial:
+          max_frames: 16
+          batch_size: 16
+          num_workers: 4
+          learning_rate: 0.0001
+          weight_decay: 0.05
+          max_epochs: 30
+          early_stop_patience: 5
+          seed: 42
+          head_hidden_dim: 256
+          head_dropout: 0.1
+          use_cosine_warmup: true
+          warmup_frac: 0.05
+          img_size: 224
+          ablation_variant: no_spatial
+          finetune: true
+          finetune_last_n_blocks: 1
+          backbone_lr: 1e-05
+    outs:
+    - path: data/06_models/ablation_no_spatial
+      hash: md5
+      md5: e618d3d180048f827ffa2f07282450f1.dir
+      size: 144736481
+      nfiles: 5
+  evaluate_ablation_no_spatial@val:
+    cmd: uv run python scripts/evaluate_ablation_global.py --data-dir 
+      data/05_model_input/val --checkpoint 
+      data/06_models/ablation_no_spatial/best_checkpoint.pt --output-dir 
+      data/08_reporting/ablation_no_spatial/val --params-path params.yaml 
+      --params-key train_ablation_no_spatial
+    deps:
+    - path: data/05_model_input/val
+      hash: md5
+      md5: f8585e64f607a7a8d7d1651a9cbc930e.dir
+      size: 77725173
+      nfiles: 3219
+    - path: data/06_models/ablation_no_spatial
+      hash: md5
+      md5: e618d3d180048f827ffa2f07282450f1.dir
+      size: 144736481
+      nfiles: 5
+    - path: scripts/evaluate_ablation_global.py
+      hash: md5
+      md5: 6d3be966e0bf9143aa20ba3228a33c67
+      size: 5251
+    - path: src/tube_multiscale_fusion/ablation_classifier.py
+      hash: md5
+      md5: 95bc858298ff235b637f7e039875f083
+      size: 8610
+    - path: src/tube_multiscale_fusion/dataset.py
+      hash: md5
+      md5: d7d3e37c27fd25f7612751b3c5869dd2
+      size: 3036
+    - path: src/tube_multiscale_fusion/eval_plots.py
+      hash: md5
+      md5: f76a38b124a936129d9e0cc6910b9e33
+      size: 3399
+    - path: src/tube_multiscale_fusion/global_branch.py
+      hash: md5
+      md5: 4cae3af1b1a020daaeeae747bd05f966
+      size: 6341
+    - path: src/tube_multiscale_fusion/lit_ablation.py
+      hash: md5
+      md5: e4608c6fa3f03dd1bc48b573602d29eb
+      size: 13172
+    params:
+      params.yaml:
+        global_branch:
+          backbone: vit_small_patch14_dinov2.lvd142m
+          aggregator:
+            num_layers: 2
+            num_heads: 6
+            ffn_dim: 1536
+            dropout: 0.1
+        train_ablation_no_spatial:
+          max_frames: 16
+          batch_size: 16
+          num_workers: 4
+          learning_rate: 0.0001
+          weight_decay: 0.05
+          max_epochs: 30
+          early_stop_patience: 5
+          seed: 42
+          head_hidden_dim: 256
+          head_dropout: 0.1
+          use_cosine_warmup: true
+          warmup_frac: 0.05
+          img_size: 224
+          ablation_variant: no_spatial
+          finetune: true
+          finetune_last_n_blocks: 1
+          backbone_lr: 1e-05
+    outs:
+    - path: data/08_reporting/ablation_no_spatial/val
+      hash: md5
+      md5: 82abdcd43aa40e3da255aece9c189a92.dir
+      size: 142108
+      nfiles: 6
+  evaluate_ablation_weighted_mean@train:
+    cmd: uv run python scripts/evaluate_ablation_global.py --data-dir 
+      data/05_model_input/train --checkpoint 
+      data/06_models/ablation_weighted_mean/best_checkpoint.pt --output-dir 
+      data/08_reporting/ablation_weighted_mean/train --params-path params.yaml 
+      --params-key train_ablation_weighted_mean
+    deps:
+    - path: data/05_model_input/train
+      hash: md5
+      md5: 840f6c912a4c6ae3103627dd32594242.dir
+      size: 843667150
+      nfiles: 34039
+    - path: data/06_models/ablation_weighted_mean
+      hash: md5
+      md5: b27b6ac456b6b49c9f2c177b4a2100ae.dir
+      size: 193430824
+      nfiles: 5
+    - path: scripts/evaluate_ablation_global.py
+      hash: md5
+      md5: 6d3be966e0bf9143aa20ba3228a33c67
+      size: 5251
+    - path: src/tube_multiscale_fusion/ablation_classifier.py
+      hash: md5
+      md5: 95bc858298ff235b637f7e039875f083
+      size: 8610
+    - path: src/tube_multiscale_fusion/dataset.py
+      hash: md5
+      md5: d7d3e37c27fd25f7612751b3c5869dd2
+      size: 3036
+    - path: src/tube_multiscale_fusion/eval_plots.py
+      hash: md5
+      md5: f76a38b124a936129d9e0cc6910b9e33
+      size: 3399
+    - path: src/tube_multiscale_fusion/fusion.py
+      hash: md5
+      md5: 695dd2af05cb3a47cc3387c9c4ba210d
+      size: 7308
+    - path: src/tube_multiscale_fusion/global_branch.py
+      hash: md5
+      md5: 4cae3af1b1a020daaeeae747bd05f966
+      size: 6341
+    - path: src/tube_multiscale_fusion/lit_ablation.py
+      hash: md5
+      md5: e4608c6fa3f03dd1bc48b573602d29eb
+      size: 13172
+    - path: src/tube_multiscale_fusion/local_branch.py
+      hash: md5
+      md5: 4a49a7286da503d7e0a21158d1c74295
+      size: 9228
+    params:
+      params.yaml:
+        fusion:
+          d_fusion: 384
+          num_layers: 2
+          num_heads: 6
+          ffn_dim: 1536
+          dropout: 0.1
+        global_branch:
+          backbone: vit_small_patch14_dinov2.lvd142m
+          aggregator:
+            num_layers: 2
+            num_heads: 6
+            ffn_dim: 1536
+            dropout: 0.1
+        local_branch:
+          grid_size: 2
+          cell_size: 112
+          tube_length: 4
+          temporal_stride: 2
+          encoder:
+            t_kernel: 2
+            h_patch: 14
+            w_patch: 14
+            embed_dim: 384
+            num_layers: 2
+            num_heads: 6
+            ffn_dim: 1536
+            dropout: 0.1
+        train_ablation_weighted_mean:
+          max_frames: 16
+          batch_size: 16
+          num_workers: 4
+          learning_rate: 0.0001
+          weight_decay: 0.05
+          max_epochs: 30
+          early_stop_patience: 5
+          seed: 42
+          head_hidden_dim: 256
+          head_dropout: 0.1
+          use_cosine_warmup: true
+          warmup_frac: 0.05
+          img_size: 224
+          ablation_variant: weighted_mean
+          finetune: true
+          finetune_last_n_blocks: 1
+          backbone_lr: 1e-05
+    outs:
+    - path: data/08_reporting/ablation_weighted_mean/train
+      hash: md5
+      md5: c335821663e048ae980ac6c29487a7a9.dir
+      size: 583368
+      nfiles: 6
+  evaluate_ablation_no_spatial@train:
+    cmd: uv run python scripts/evaluate_ablation_global.py --data-dir 
+      data/05_model_input/train --checkpoint 
+      data/06_models/ablation_no_spatial/best_checkpoint.pt --output-dir 
+      data/08_reporting/ablation_no_spatial/train --params-path params.yaml 
+      --params-key train_ablation_no_spatial
+    deps:
+    - path: data/05_model_input/train
+      hash: md5
+      md5: 840f6c912a4c6ae3103627dd32594242.dir
+      size: 843667150
+      nfiles: 34039
+    - path: data/06_models/ablation_no_spatial
+      hash: md5
+      md5: e618d3d180048f827ffa2f07282450f1.dir
+      size: 144736481
+      nfiles: 5
+    - path: scripts/evaluate_ablation_global.py
+      hash: md5
+      md5: 6d3be966e0bf9143aa20ba3228a33c67
+      size: 5251
+    - path: src/tube_multiscale_fusion/ablation_classifier.py
+      hash: md5
+      md5: 95bc858298ff235b637f7e039875f083
+      size: 8610
+    - path: src/tube_multiscale_fusion/dataset.py
+      hash: md5
+      md5: d7d3e37c27fd25f7612751b3c5869dd2
+      size: 3036
+    - path: src/tube_multiscale_fusion/eval_plots.py
+      hash: md5
+      md5: f76a38b124a936129d9e0cc6910b9e33
+      size: 3399
+    - path: src/tube_multiscale_fusion/global_branch.py
+      hash: md5
+      md5: 4cae3af1b1a020daaeeae747bd05f966
+      size: 6341
+    - path: src/tube_multiscale_fusion/lit_ablation.py
+      hash: md5
+      md5: e4608c6fa3f03dd1bc48b573602d29eb
+      size: 13172
+    params:
+      params.yaml:
+        global_branch:
+          backbone: vit_small_patch14_dinov2.lvd142m
+          aggregator:
+            num_layers: 2
+            num_heads: 6
+            ffn_dim: 1536
+            dropout: 0.1
+        train_ablation_no_spatial:
+          max_frames: 16
+          batch_size: 16
+          num_workers: 4
+          learning_rate: 0.0001
+          weight_decay: 0.05
+          max_epochs: 30
+          early_stop_patience: 5
+          seed: 42
+          head_hidden_dim: 256
+          head_dropout: 0.1
+          use_cosine_warmup: true
+          warmup_frac: 0.05
+          img_size: 224
+          ablation_variant: no_spatial
+          finetune: true
+          finetune_last_n_blocks: 1
+          backbone_lr: 1e-05
+    outs:
+    - path: data/08_reporting/ablation_no_spatial/train
+      hash: md5
+      md5: a196ef28362828c199fa1358af6980f4.dir
+      size: 580956
+      nfiles: 6
+  train_temporal@gru:
+    cmd: uv run python scripts/train_ablation_global.py --train-dir 
+      data/05_model_input/train --val-dir data/05_model_input/val --output-dir 
+      data/06_models/temporal_gru --params-path params.yaml --params-key 
+      train_temporal_gru
+    deps:
+    - path: data/05_model_input/train
+      hash: md5
+      md5: 840f6c912a4c6ae3103627dd32594242.dir
+      size: 843667150
+      nfiles: 34039
+    - path: data/05_model_input/val
+      hash: md5
+      md5: f8585e64f607a7a8d7d1651a9cbc930e.dir
+      size: 77725173
+      nfiles: 3219
+    - path: scripts/train_ablation_global.py
+      hash: md5
+      md5: 6678a014e3b92ce2046ebd6b8f90f3f7
+      size: 5301
+    - path: src/tube_multiscale_fusion/ablation_classifier.py
+      hash: md5
+      md5: 1c4f4f7de41e3253d389d3f4314fc4d0
+      size: 8701
+    - path: src/tube_multiscale_fusion/augment.py
+      hash: md5
+      md5: f52a42f92caa2fac859bf3a81a18971d
+      size: 9489
+    - path: src/tube_multiscale_fusion/dataset.py
+      hash: md5
+      md5: d7d3e37c27fd25f7612751b3c5869dd2
+      size: 3036
+    - path: src/tube_multiscale_fusion/global_branch.py
+      hash: md5
+      md5: 22bcbb284170752ff61a781a6119a474
+      size: 11333
+    - path: src/tube_multiscale_fusion/lit_ablation.py
+      hash: md5
+      md5: e09db331d78b94f700a33fadbdedd246
+      size: 13267
+    params:
+      params.yaml:
+        augment:
+          enabled: true
+          spatial:
+            flip_prob: 0.5
+            rotation_deg: 5.0
+            scale_range:
+            - 0.9
+            - 1.1
+            translate_frac: 0.05
+          photometric:
+            brightness_range:
+            - 0.8
+            - 1.2
+            contrast_range:
+            - 0.8
+            - 1.2
+            saturation_range:
+            - 0.8
+            - 1.2
+          temporal:
+            subseq_prob: 0.5
+            subseq_min_len: 4
+            stride_prob: 0.25
+            frame_drop_prob: 0.15
+            min_valid_after_drop: 4
+        global_branch:
+          backbone: vit_small_patch14_dinov2.lvd142m
+          aggregator:
+            num_layers: 2
+            num_heads: 6
+            ffn_dim: 1536
+            dropout: 0.1
+        train_temporal_gru:
+          max_frames: 16
+          batch_size: 16
+          num_workers: 4
+          learning_rate: 0.0001
+          weight_decay: 0.05
+          max_epochs: 30
+          early_stop_patience: 5
+          seed: 42
+          head_hidden_dim: 256
+          head_dropout: 0.1
+          use_cosine_warmup: true
+          warmup_frac: 0.05
+          img_size: 224
+          ablation_variant: no_spatial
+          finetune: true
+          finetune_last_n_blocks: 1
+          backbone_lr: 1e-05
+          aggregator_kind: gru
+    outs:
+    - path: data/06_models/temporal_gru
+      hash: md5
+      md5: f8cf358b58419f54ddb7f699f5fec716.dir
+      size: 112687616
+      nfiles: 5
+  evaluate_temporal@gru-val:
+    cmd: uv run python scripts/evaluate_ablation_global.py --data-dir 
+      data/05_model_input/val --checkpoint 
+      data/06_models/temporal_gru/best_checkpoint.pt --output-dir 
+      data/08_reporting/temporal_gru/val --params-path params.yaml --params-key 
+      train_temporal_gru
+    deps:
+    - path: data/05_model_input/val
+      hash: md5
+      md5: f8585e64f607a7a8d7d1651a9cbc930e.dir
+      size: 77725173
+      nfiles: 3219
+    - path: data/06_models/temporal_gru
+      hash: md5
+      md5: f8cf358b58419f54ddb7f699f5fec716.dir
+      size: 112687616
+      nfiles: 5
+    - path: scripts/evaluate_ablation_global.py
+      hash: md5
+      md5: 6d3be966e0bf9143aa20ba3228a33c67
+      size: 5251
+    - path: src/tube_multiscale_fusion/ablation_classifier.py
+      hash: md5
+      md5: 1c4f4f7de41e3253d389d3f4314fc4d0
+      size: 8701
+    - path: src/tube_multiscale_fusion/dataset.py
+      hash: md5
+      md5: d7d3e37c27fd25f7612751b3c5869dd2
+      size: 3036
+    - path: src/tube_multiscale_fusion/eval_plots.py
+      hash: md5
+      md5: f76a38b124a936129d9e0cc6910b9e33
+      size: 3399
+    - path: src/tube_multiscale_fusion/global_branch.py
+      hash: md5
+      md5: 22bcbb284170752ff61a781a6119a474
+      size: 11333
+    - path: src/tube_multiscale_fusion/lit_ablation.py
+      hash: md5
+      md5: e09db331d78b94f700a33fadbdedd246
+      size: 13267
+    params:
+      params.yaml:
+        global_branch:
+          backbone: vit_small_patch14_dinov2.lvd142m
+          aggregator:
+            num_layers: 2
+            num_heads: 6
+            ffn_dim: 1536
+            dropout: 0.1
+        train_temporal_gru:
+          max_frames: 16
+          batch_size: 16
+          num_workers: 4
+          learning_rate: 0.0001
+          weight_decay: 0.05
+          max_epochs: 30
+          early_stop_patience: 5
+          seed: 42
+          head_hidden_dim: 256
+          head_dropout: 0.1
+          use_cosine_warmup: true
+          warmup_frac: 0.05
+          img_size: 224
+          ablation_variant: no_spatial
+          finetune: true
+          finetune_last_n_blocks: 1
+          backbone_lr: 1e-05
+          aggregator_kind: gru
+    outs:
+    - path: data/08_reporting/temporal_gru/val
+      hash: md5
+      md5: 9cbfe7ef1da2666bf0f38e25fb8721cb.dir
+      size: 142392
+      nfiles: 6
+  train_temporal@conv1d:
+    cmd: uv run python scripts/train_ablation_global.py --train-dir 
+      data/05_model_input/train --val-dir data/05_model_input/val --output-dir 
+      data/06_models/temporal_conv1d --params-path params.yaml --params-key 
+      train_temporal_conv1d
+    deps:
+    - path: data/05_model_input/train
+      hash: md5
+      md5: 840f6c912a4c6ae3103627dd32594242.dir
+      size: 843667150
+      nfiles: 34039
+    - path: data/05_model_input/val
+      hash: md5
+      md5: f8585e64f607a7a8d7d1651a9cbc930e.dir
+      size: 77725173
+      nfiles: 3219
+    - path: scripts/train_ablation_global.py
+      hash: md5
+      md5: 6678a014e3b92ce2046ebd6b8f90f3f7
+      size: 5301
+    - path: src/tube_multiscale_fusion/ablation_classifier.py
+      hash: md5
+      md5: 1c4f4f7de41e3253d389d3f4314fc4d0
+      size: 8701
+    - path: src/tube_multiscale_fusion/augment.py
+      hash: md5
+      md5: f52a42f92caa2fac859bf3a81a18971d
+      size: 9489
+    - path: src/tube_multiscale_fusion/dataset.py
+      hash: md5
+      md5: d7d3e37c27fd25f7612751b3c5869dd2
+      size: 3036
+    - path: src/tube_multiscale_fusion/global_branch.py
+      hash: md5
+      md5: 22bcbb284170752ff61a781a6119a474
+      size: 11333
+    - path: src/tube_multiscale_fusion/lit_ablation.py
+      hash: md5
+      md5: e09db331d78b94f700a33fadbdedd246
+      size: 13267
+    params:
+      params.yaml:
+        augment:
+          enabled: true
+          spatial:
+            flip_prob: 0.5
+            rotation_deg: 5.0
+            scale_range:
+            - 0.9
+            - 1.1
+            translate_frac: 0.05
+          photometric:
+            brightness_range:
+            - 0.8
+            - 1.2
+            contrast_range:
+            - 0.8
+            - 1.2
+            saturation_range:
+            - 0.8
+            - 1.2
+          temporal:
+            subseq_prob: 0.5
+            subseq_min_len: 4
+            stride_prob: 0.25
+            frame_drop_prob: 0.15
+            min_valid_after_drop: 4
+        global_branch:
+          backbone: vit_small_patch14_dinov2.lvd142m
+          aggregator:
+            num_layers: 2
+            num_heads: 6
+            ffn_dim: 1536
+            dropout: 0.1
+        train_temporal_conv1d:
+          max_frames: 16
+          batch_size: 16
+          num_workers: 4
+          learning_rate: 0.0001
+          weight_decay: 0.05
+          max_epochs: 30
+          early_stop_patience: 5
+          seed: 42
+          head_hidden_dim: 256
+          head_dropout: 0.1
+          use_cosine_warmup: true
+          warmup_frac: 0.05
+          img_size: 224
+          ablation_variant: no_spatial
+          finetune: true
+          finetune_last_n_blocks: 1
+          backbone_lr: 1e-05
+          aggregator_kind: conv1d
+    outs:
+    - path: data/06_models/temporal_conv1d
+      hash: md5
+      md5: 69be5b9da4e9b1e069222866c9dd06c3.dir
+      size: 112664954
+      nfiles: 5
+  evaluate_temporal@conv1d-val:
+    cmd: uv run python scripts/evaluate_ablation_global.py --data-dir 
+      data/05_model_input/val --checkpoint 
+      data/06_models/temporal_conv1d/best_checkpoint.pt --output-dir 
+      data/08_reporting/temporal_conv1d/val --params-path params.yaml 
+      --params-key train_temporal_conv1d
+    deps:
+    - path: data/05_model_input/val
+      hash: md5
+      md5: f8585e64f607a7a8d7d1651a9cbc930e.dir
+      size: 77725173
+      nfiles: 3219
+    - path: data/06_models/temporal_conv1d
+      hash: md5
+      md5: 69be5b9da4e9b1e069222866c9dd06c3.dir
+      size: 112664954
+      nfiles: 5
+    - path: scripts/evaluate_ablation_global.py
+      hash: md5
+      md5: 6d3be966e0bf9143aa20ba3228a33c67
+      size: 5251
+    - path: src/tube_multiscale_fusion/ablation_classifier.py
+      hash: md5
+      md5: 1c4f4f7de41e3253d389d3f4314fc4d0
+      size: 8701
+    - path: src/tube_multiscale_fusion/dataset.py
+      hash: md5
+      md5: d7d3e37c27fd25f7612751b3c5869dd2
+      size: 3036
+    - path: src/tube_multiscale_fusion/eval_plots.py
+      hash: md5
+      md5: f76a38b124a936129d9e0cc6910b9e33
+      size: 3399
+    - path: src/tube_multiscale_fusion/global_branch.py
+      hash: md5
+      md5: 22bcbb284170752ff61a781a6119a474
+      size: 11333
+    - path: src/tube_multiscale_fusion/lit_ablation.py
+      hash: md5
+      md5: e09db331d78b94f700a33fadbdedd246
+      size: 13267
+    params:
+      params.yaml:
+        global_branch:
+          backbone: vit_small_patch14_dinov2.lvd142m
+          aggregator:
+            num_layers: 2
+            num_heads: 6
+            ffn_dim: 1536
+            dropout: 0.1
+        train_temporal_conv1d:
+          max_frames: 16
+          batch_size: 16
+          num_workers: 4
+          learning_rate: 0.0001
+          weight_decay: 0.05
+          max_epochs: 30
+          early_stop_patience: 5
+          seed: 42
+          head_hidden_dim: 256
+          head_dropout: 0.1
+          use_cosine_warmup: true
+          warmup_frac: 0.05
+          img_size: 224
+          ablation_variant: no_spatial
+          finetune: true
+          finetune_last_n_blocks: 1
+          backbone_lr: 1e-05
+          aggregator_kind: conv1d
+    outs:
+    - path: data/08_reporting/temporal_conv1d/val
+      hash: md5
+      md5: 231414adf4e44e68c3d96a3e3df6645d.dir
+      size: 143841
+      nfiles: 6
+  train_temporal@lstm:
+    cmd: uv run python scripts/train_ablation_global.py --train-dir 
+      data/05_model_input/train --val-dir data/05_model_input/val --output-dir 
+      data/06_models/temporal_lstm --params-path params.yaml --params-key 
+      train_temporal_lstm
+    deps:
+    - path: data/05_model_input/train
+      hash: md5
+      md5: 840f6c912a4c6ae3103627dd32594242.dir
+      size: 843667150
+      nfiles: 34039
+    - path: data/05_model_input/val
+      hash: md5
+      md5: f8585e64f607a7a8d7d1651a9cbc930e.dir
+      size: 77725173
+      nfiles: 3219
+    - path: scripts/train_ablation_global.py
+      hash: md5
+      md5: 6678a014e3b92ce2046ebd6b8f90f3f7
+      size: 5301
+    - path: src/tube_multiscale_fusion/ablation_classifier.py
+      hash: md5
+      md5: 1c4f4f7de41e3253d389d3f4314fc4d0
+      size: 8701
+    - path: src/tube_multiscale_fusion/augment.py
+      hash: md5
+      md5: f52a42f92caa2fac859bf3a81a18971d
+      size: 9489
+    - path: src/tube_multiscale_fusion/dataset.py
+      hash: md5
+      md5: d7d3e37c27fd25f7612751b3c5869dd2
+      size: 3036
+    - path: src/tube_multiscale_fusion/global_branch.py
+      hash: md5
+      md5: 22bcbb284170752ff61a781a6119a474
+      size: 11333
+    - path: src/tube_multiscale_fusion/lit_ablation.py
+      hash: md5
+      md5: e09db331d78b94f700a33fadbdedd246
+      size: 13267
+    params:
+      params.yaml:
+        augment:
+          enabled: true
+          spatial:
+            flip_prob: 0.5
+            rotation_deg: 5.0
+            scale_range:
+            - 0.9
+            - 1.1
+            translate_frac: 0.05
+          photometric:
+            brightness_range:
+            - 0.8
+            - 1.2
+            contrast_range:
+            - 0.8
+            - 1.2
+            saturation_range:
+            - 0.8
+            - 1.2
+          temporal:
+            subseq_prob: 0.5
+            subseq_min_len: 4
+            stride_prob: 0.25
+            frame_drop_prob: 0.15
+            min_valid_after_drop: 4
+        global_branch:
+          backbone: vit_small_patch14_dinov2.lvd142m
+          aggregator:
+            num_layers: 2
+            num_heads: 6
+            ffn_dim: 1536
+            dropout: 0.1
+        train_temporal_lstm:
+          max_frames: 16
+          batch_size: 16
+          num_workers: 4
+          learning_rate: 0.0001
+          weight_decay: 0.05
+          max_epochs: 30
+          early_stop_patience: 5
+          seed: 42
+          head_hidden_dim: 256
+          head_dropout: 0.1
+          use_cosine_warmup: true
+          warmup_frac: 0.05
+          img_size: 224
+          ablation_variant: no_spatial
+          finetune: true
+          finetune_last_n_blocks: 1
+          backbone_lr: 1e-05
+          aggregator_kind: lstm
+    outs:
+    - path: data/06_models/temporal_lstm
+      hash: md5
+      md5: b9d77529a81befef12ec2555bcc18352.dir
+      size: 116259757
+      nfiles: 5
+  evaluate_temporal@lstm-train:
+    cmd: uv run python scripts/evaluate_ablation_global.py --data-dir 
+      data/05_model_input/train --checkpoint 
+      data/06_models/temporal_lstm/best_checkpoint.pt --output-dir 
+      data/08_reporting/temporal_lstm/train --params-path params.yaml 
+      --params-key train_temporal_lstm
+    deps:
+    - path: data/05_model_input/train
+      hash: md5
+      md5: 840f6c912a4c6ae3103627dd32594242.dir
+      size: 843667150
+      nfiles: 34039
+    - path: data/06_models/temporal_lstm
+      hash: md5
+      md5: b9d77529a81befef12ec2555bcc18352.dir
+      size: 116259757
+      nfiles: 5
+    - path: scripts/evaluate_ablation_global.py
+      hash: md5
+      md5: 6d3be966e0bf9143aa20ba3228a33c67
+      size: 5251
+    - path: src/tube_multiscale_fusion/ablation_classifier.py
+      hash: md5
+      md5: 1c4f4f7de41e3253d389d3f4314fc4d0
+      size: 8701
+    - path: src/tube_multiscale_fusion/dataset.py
+      hash: md5
+      md5: d7d3e37c27fd25f7612751b3c5869dd2
+      size: 3036
+    - path: src/tube_multiscale_fusion/eval_plots.py
+      hash: md5
+      md5: f76a38b124a936129d9e0cc6910b9e33
+      size: 3399
+    - path: src/tube_multiscale_fusion/global_branch.py
+      hash: md5
+      md5: 22bcbb284170752ff61a781a6119a474
+      size: 11333
+    - path: src/tube_multiscale_fusion/lit_ablation.py
+      hash: md5
+      md5: e09db331d78b94f700a33fadbdedd246
+      size: 13267
+    params:
+      params.yaml:
+        global_branch:
+          backbone: vit_small_patch14_dinov2.lvd142m
+          aggregator:
+            num_layers: 2
+            num_heads: 6
+            ffn_dim: 1536
+            dropout: 0.1
+        train_temporal_lstm:
+          max_frames: 16
+          batch_size: 16
+          num_workers: 4
+          learning_rate: 0.0001
+          weight_decay: 0.05
+          max_epochs: 30
+          early_stop_patience: 5
+          seed: 42
+          head_hidden_dim: 256
+          head_dropout: 0.1
+          use_cosine_warmup: true
+          warmup_frac: 0.05
+          img_size: 224
+          ablation_variant: no_spatial
+          finetune: true
+          finetune_last_n_blocks: 1
+          backbone_lr: 1e-05
+          aggregator_kind: lstm
+    outs:
+    - path: data/08_reporting/temporal_lstm/train
+      hash: md5
+      md5: 9043d6e8d6b7a9bfd1180f82a889089b.dir
+      size: 579279
+      nfiles: 6
+  train_temporal@linear_wavg:
+    cmd: uv run python scripts/train_ablation_global.py --train-dir 
+      data/05_model_input/train --val-dir data/05_model_input/val --output-dir 
+      data/06_models/temporal_linear_wavg --params-path params.yaml --params-key
+      train_temporal_linear_wavg
+    deps:
+    - path: data/05_model_input/train
+      hash: md5
+      md5: 840f6c912a4c6ae3103627dd32594242.dir
+      size: 843667150
+      nfiles: 34039
+    - path: data/05_model_input/val
+      hash: md5
+      md5: f8585e64f607a7a8d7d1651a9cbc930e.dir
+      size: 77725173
+      nfiles: 3219
+    - path: scripts/train_ablation_global.py
+      hash: md5
+      md5: 6678a014e3b92ce2046ebd6b8f90f3f7
+      size: 5301
+    - path: src/tube_multiscale_fusion/ablation_classifier.py
+      hash: md5
+      md5: 1c4f4f7de41e3253d389d3f4314fc4d0
+      size: 8701
+    - path: src/tube_multiscale_fusion/augment.py
+      hash: md5
+      md5: f52a42f92caa2fac859bf3a81a18971d
+      size: 9489
+    - path: src/tube_multiscale_fusion/dataset.py
+      hash: md5
+      md5: d7d3e37c27fd25f7612751b3c5869dd2
+      size: 3036
+    - path: src/tube_multiscale_fusion/global_branch.py
+      hash: md5
+      md5: 22bcbb284170752ff61a781a6119a474
+      size: 11333
+    - path: src/tube_multiscale_fusion/lit_ablation.py
+      hash: md5
+      md5: e09db331d78b94f700a33fadbdedd246
+      size: 13267
+    params:
+      params.yaml:
+        augment:
+          enabled: true
+          spatial:
+            flip_prob: 0.5
+            rotation_deg: 5.0
+            scale_range:
+            - 0.9
+            - 1.1
+            translate_frac: 0.05
+          photometric:
+            brightness_range:
+            - 0.8
+            - 1.2
+            contrast_range:
+            - 0.8
+            - 1.2
+            saturation_range:
+            - 0.8
+            - 1.2
+          temporal:
+            subseq_prob: 0.5
+            subseq_min_len: 4
+            stride_prob: 0.25
+            frame_drop_prob: 0.15
+            min_valid_after_drop: 4
+        global_branch:
+          backbone: vit_small_patch14_dinov2.lvd142m
+          aggregator:
+            num_layers: 2
+            num_heads: 6
+            ffn_dim: 1536
+            dropout: 0.1
+        train_temporal_linear_wavg:
+          max_frames: 16
+          batch_size: 16
+          num_workers: 4
+          learning_rate: 0.0001
+          weight_decay: 0.05
+          max_epochs: 30
+          early_stop_patience: 5
+          seed: 42
+          head_hidden_dim: 256
+          head_dropout: 0.1
+          use_cosine_warmup: true
+          warmup_frac: 0.05
+          img_size: 224
+          ablation_variant: no_spatial
+          finetune: true
+          finetune_last_n_blocks: 1
+          backbone_lr: 1e-05
+          aggregator_kind: linear_wavg
+    outs:
+    - path: data/06_models/temporal_linear_wavg
+      hash: md5
+      md5: d8963d9ef59a5ef56448ed72c5c34511.dir
+      size: 103811952
+      nfiles: 5
+  evaluate_temporal@linear_wavg-val:
+    cmd: uv run python scripts/evaluate_ablation_global.py --data-dir 
+      data/05_model_input/val --checkpoint 
+      data/06_models/temporal_linear_wavg/best_checkpoint.pt --output-dir 
+      data/08_reporting/temporal_linear_wavg/val --params-path params.yaml 
+      --params-key train_temporal_linear_wavg
+    deps:
+    - path: data/05_model_input/val
+      hash: md5
+      md5: f8585e64f607a7a8d7d1651a9cbc930e.dir
+      size: 77725173
+      nfiles: 3219
+    - path: data/06_models/temporal_linear_wavg
+      hash: md5
+      md5: d8963d9ef59a5ef56448ed72c5c34511.dir
+      size: 103811952
+      nfiles: 5
+    - path: scripts/evaluate_ablation_global.py
+      hash: md5
+      md5: 6d3be966e0bf9143aa20ba3228a33c67
+      size: 5251
+    - path: src/tube_multiscale_fusion/ablation_classifier.py
+      hash: md5
+      md5: 1c4f4f7de41e3253d389d3f4314fc4d0
+      size: 8701
+    - path: src/tube_multiscale_fusion/dataset.py
+      hash: md5
+      md5: d7d3e37c27fd25f7612751b3c5869dd2
+      size: 3036
+    - path: src/tube_multiscale_fusion/eval_plots.py
+      hash: md5
+      md5: f76a38b124a936129d9e0cc6910b9e33
+      size: 3399
+    - path: src/tube_multiscale_fusion/global_branch.py
+      hash: md5
+      md5: 22bcbb284170752ff61a781a6119a474
+      size: 11333
+    - path: src/tube_multiscale_fusion/lit_ablation.py
+      hash: md5
+      md5: e09db331d78b94f700a33fadbdedd246
+      size: 13267
+    params:
+      params.yaml:
+        global_branch:
+          backbone: vit_small_patch14_dinov2.lvd142m
+          aggregator:
+            num_layers: 2
+            num_heads: 6
+            ffn_dim: 1536
+            dropout: 0.1
+        train_temporal_linear_wavg:
+          max_frames: 16
+          batch_size: 16
+          num_workers: 4
+          learning_rate: 0.0001
+          weight_decay: 0.05
+          max_epochs: 30
+          early_stop_patience: 5
+          seed: 42
+          head_hidden_dim: 256
+          head_dropout: 0.1
+          use_cosine_warmup: true
+          warmup_frac: 0.05
+          img_size: 224
+          ablation_variant: no_spatial
+          finetune: true
+          finetune_last_n_blocks: 1
+          backbone_lr: 1e-05
+          aggregator_kind: linear_wavg
+    outs:
+    - path: data/08_reporting/temporal_linear_wavg/val
+      hash: md5
+      md5: 8d8acb1b795fd7f2c237d4fe6deb6af5.dir
+      size: 145067
+      nfiles: 6
+  train_temporal@mlp:
+    cmd: uv run python scripts/train_ablation_global.py --train-dir 
+      data/05_model_input/train --val-dir data/05_model_input/val --output-dir 
+      data/06_models/temporal_mlp --params-path params.yaml --params-key 
+      train_temporal_mlp
+    deps:
+    - path: data/05_model_input/train
+      hash: md5
+      md5: 840f6c912a4c6ae3103627dd32594242.dir
+      size: 843667150
+      nfiles: 34039
+    - path: data/05_model_input/val
+      hash: md5
+      md5: f8585e64f607a7a8d7d1651a9cbc930e.dir
+      size: 77725173
+      nfiles: 3219
+    - path: scripts/train_ablation_global.py
+      hash: md5
+      md5: 6678a014e3b92ce2046ebd6b8f90f3f7
+      size: 5301
+    - path: src/tube_multiscale_fusion/ablation_classifier.py
+      hash: md5
+      md5: 1c4f4f7de41e3253d389d3f4314fc4d0
+      size: 8701
+    - path: src/tube_multiscale_fusion/augment.py
+      hash: md5
+      md5: f52a42f92caa2fac859bf3a81a18971d
+      size: 9489
+    - path: src/tube_multiscale_fusion/dataset.py
+      hash: md5
+      md5: d7d3e37c27fd25f7612751b3c5869dd2
+      size: 3036
+    - path: src/tube_multiscale_fusion/global_branch.py
+      hash: md5
+      md5: 22bcbb284170752ff61a781a6119a474
+      size: 11333
+    - path: src/tube_multiscale_fusion/lit_ablation.py
+      hash: md5
+      md5: e09db331d78b94f700a33fadbdedd246
+      size: 13267
+    params:
+      params.yaml:
+        augment:
+          enabled: true
+          spatial:
+            flip_prob: 0.5
+            rotation_deg: 5.0
+            scale_range:
+            - 0.9
+            - 1.1
+            translate_frac: 0.05
+          photometric:
+            brightness_range:
+            - 0.8
+            - 1.2
+            contrast_range:
+            - 0.8
+            - 1.2
+            saturation_range:
+            - 0.8
+            - 1.2
+          temporal:
+            subseq_prob: 0.5
+            subseq_min_len: 4
+            stride_prob: 0.25
+            frame_drop_prob: 0.15
+            min_valid_after_drop: 4
+        global_branch:
+          backbone: vit_small_patch14_dinov2.lvd142m
+          aggregator:
+            num_layers: 2
+            num_heads: 6
+            ffn_dim: 1536
+            dropout: 0.1
+        train_temporal_mlp:
+          max_frames: 16
+          batch_size: 16
+          num_workers: 4
+          learning_rate: 0.0001
+          weight_decay: 0.05
+          max_epochs: 30
+          early_stop_patience: 5
+          seed: 42
+          head_hidden_dim: 256
+          head_dropout: 0.1
+          use_cosine_warmup: true
+          warmup_frac: 0.05
+          img_size: 224
+          ablation_variant: no_spatial
+          finetune: true
+          finetune_last_n_blocks: 1
+          backbone_lr: 1e-05
+          aggregator_kind: mlp
+    outs:
+    - path: data/06_models/temporal_mlp
+      hash: md5
+      md5: 9a4eec86025bd02e640b39aedfc7b252.dir
+      size: 182282337
+      nfiles: 5
+  evaluate_temporal@conv1d-train:
+    cmd: uv run python scripts/evaluate_ablation_global.py --data-dir 
+      data/05_model_input/train --checkpoint 
+      data/06_models/temporal_conv1d/best_checkpoint.pt --output-dir 
+      data/08_reporting/temporal_conv1d/train --params-path params.yaml 
+      --params-key train_temporal_conv1d
+    deps:
+    - path: data/05_model_input/train
+      hash: md5
+      md5: 840f6c912a4c6ae3103627dd32594242.dir
+      size: 843667150
+      nfiles: 34039
+    - path: data/06_models/temporal_conv1d
+      hash: md5
+      md5: 69be5b9da4e9b1e069222866c9dd06c3.dir
+      size: 112664954
+      nfiles: 5
+    - path: scripts/evaluate_ablation_global.py
+      hash: md5
+      md5: 6d3be966e0bf9143aa20ba3228a33c67
+      size: 5251
+    - path: src/tube_multiscale_fusion/ablation_classifier.py
+      hash: md5
+      md5: 1c4f4f7de41e3253d389d3f4314fc4d0
+      size: 8701
+    - path: src/tube_multiscale_fusion/dataset.py
+      hash: md5
+      md5: d7d3e37c27fd25f7612751b3c5869dd2
+      size: 3036
+    - path: src/tube_multiscale_fusion/eval_plots.py
+      hash: md5
+      md5: f76a38b124a936129d9e0cc6910b9e33
+      size: 3399
+    - path: src/tube_multiscale_fusion/global_branch.py
+      hash: md5
+      md5: 22bcbb284170752ff61a781a6119a474
+      size: 11333
+    - path: src/tube_multiscale_fusion/lit_ablation.py
+      hash: md5
+      md5: e09db331d78b94f700a33fadbdedd246
+      size: 13267
+    params:
+      params.yaml:
+        global_branch:
+          backbone: vit_small_patch14_dinov2.lvd142m
+          aggregator:
+            num_layers: 2
+            num_heads: 6
+            ffn_dim: 1536
+            dropout: 0.1
+        train_temporal_conv1d:
+          max_frames: 16
+          batch_size: 16
+          num_workers: 4
+          learning_rate: 0.0001
+          weight_decay: 0.05
+          max_epochs: 30
+          early_stop_patience: 5
+          seed: 42
+          head_hidden_dim: 256
+          head_dropout: 0.1
+          use_cosine_warmup: true
+          warmup_frac: 0.05
+          img_size: 224
+          ablation_variant: no_spatial
+          finetune: true
+          finetune_last_n_blocks: 1
+          backbone_lr: 1e-05
+          aggregator_kind: conv1d
+    outs:
+    - path: data/08_reporting/temporal_conv1d/train
+      hash: md5
+      md5: 1ae2f543d3e55f0e3a2567302f1aef89.dir
+      size: 582314
+      nfiles: 6
+  evaluate_temporal@lstm-val:
+    cmd: uv run python scripts/evaluate_ablation_global.py --data-dir 
+      data/05_model_input/val --checkpoint 
+      data/06_models/temporal_lstm/best_checkpoint.pt --output-dir 
+      data/08_reporting/temporal_lstm/val --params-path params.yaml --params-key
+      train_temporal_lstm
+    deps:
+    - path: data/05_model_input/val
+      hash: md5
+      md5: f8585e64f607a7a8d7d1651a9cbc930e.dir
+      size: 77725173
+      nfiles: 3219
+    - path: data/06_models/temporal_lstm
+      hash: md5
+      md5: b9d77529a81befef12ec2555bcc18352.dir
+      size: 116259757
+      nfiles: 5
+    - path: scripts/evaluate_ablation_global.py
+      hash: md5
+      md5: 6d3be966e0bf9143aa20ba3228a33c67
+      size: 5251
+    - path: src/tube_multiscale_fusion/ablation_classifier.py
+      hash: md5
+      md5: 1c4f4f7de41e3253d389d3f4314fc4d0
+      size: 8701
+    - path: src/tube_multiscale_fusion/dataset.py
+      hash: md5
+      md5: d7d3e37c27fd25f7612751b3c5869dd2
+      size: 3036
+    - path: src/tube_multiscale_fusion/eval_plots.py
+      hash: md5
+      md5: f76a38b124a936129d9e0cc6910b9e33
+      size: 3399
+    - path: src/tube_multiscale_fusion/global_branch.py
+      hash: md5
+      md5: 22bcbb284170752ff61a781a6119a474
+      size: 11333
+    - path: src/tube_multiscale_fusion/lit_ablation.py
+      hash: md5
+      md5: e09db331d78b94f700a33fadbdedd246
+      size: 13267
+    params:
+      params.yaml:
+        global_branch:
+          backbone: vit_small_patch14_dinov2.lvd142m
+          aggregator:
+            num_layers: 2
+            num_heads: 6
+            ffn_dim: 1536
+            dropout: 0.1
+        train_temporal_lstm:
+          max_frames: 16
+          batch_size: 16
+          num_workers: 4
+          learning_rate: 0.0001
+          weight_decay: 0.05
+          max_epochs: 30
+          early_stop_patience: 5
+          seed: 42
+          head_hidden_dim: 256
+          head_dropout: 0.1
+          use_cosine_warmup: true
+          warmup_frac: 0.05
+          img_size: 224
+          ablation_variant: no_spatial
+          finetune: true
+          finetune_last_n_blocks: 1
+          backbone_lr: 1e-05
+          aggregator_kind: lstm
+    outs:
+    - path: data/08_reporting/temporal_lstm/val
+      hash: md5
+      md5: 2c114406b3ea840825aa0ee086697183.dir
+      size: 143625
+      nfiles: 6
+  evaluate_temporal@gru-train:
+    cmd: uv run python scripts/evaluate_ablation_global.py --data-dir 
+      data/05_model_input/train --checkpoint 
+      data/06_models/temporal_gru/best_checkpoint.pt --output-dir 
+      data/08_reporting/temporal_gru/train --params-path params.yaml 
+      --params-key train_temporal_gru
+    deps:
+    - path: data/05_model_input/train
+      hash: md5
+      md5: 840f6c912a4c6ae3103627dd32594242.dir
+      size: 843667150
+      nfiles: 34039
+    - path: data/06_models/temporal_gru
+      hash: md5
+      md5: f8cf358b58419f54ddb7f699f5fec716.dir
+      size: 112687616
+      nfiles: 5
+    - path: scripts/evaluate_ablation_global.py
+      hash: md5
+      md5: 6d3be966e0bf9143aa20ba3228a33c67
+      size: 5251
+    - path: src/tube_multiscale_fusion/ablation_classifier.py
+      hash: md5
+      md5: 1c4f4f7de41e3253d389d3f4314fc4d0
+      size: 8701
+    - path: src/tube_multiscale_fusion/dataset.py
+      hash: md5
+      md5: d7d3e37c27fd25f7612751b3c5869dd2
+      size: 3036
+    - path: src/tube_multiscale_fusion/eval_plots.py
+      hash: md5
+      md5: f76a38b124a936129d9e0cc6910b9e33
+      size: 3399
+    - path: src/tube_multiscale_fusion/global_branch.py
+      hash: md5
+      md5: 22bcbb284170752ff61a781a6119a474
+      size: 11333
+    - path: src/tube_multiscale_fusion/lit_ablation.py
+      hash: md5
+      md5: e09db331d78b94f700a33fadbdedd246
+      size: 13267
+    params:
+      params.yaml:
+        global_branch:
+          backbone: vit_small_patch14_dinov2.lvd142m
+          aggregator:
+            num_layers: 2
+            num_heads: 6
+            ffn_dim: 1536
+            dropout: 0.1
+        train_temporal_gru:
+          max_frames: 16
+          batch_size: 16
+          num_workers: 4
+          learning_rate: 0.0001
+          weight_decay: 0.05
+          max_epochs: 30
+          early_stop_patience: 5
+          seed: 42
+          head_hidden_dim: 256
+          head_dropout: 0.1
+          use_cosine_warmup: true
+          warmup_frac: 0.05
+          img_size: 224
+          ablation_variant: no_spatial
+          finetune: true
+          finetune_last_n_blocks: 1
+          backbone_lr: 1e-05
+          aggregator_kind: gru
+    outs:
+    - path: data/08_reporting/temporal_gru/train
+      hash: md5
+      md5: d2efc1ef9e63e960e543c255c40c3c70.dir
+      size: 581035
+      nfiles: 6
+  evaluate_temporal@linear_wavg-train:
+    cmd: uv run python scripts/evaluate_ablation_global.py --data-dir 
+      data/05_model_input/train --checkpoint 
+      data/06_models/temporal_linear_wavg/best_checkpoint.pt --output-dir 
+      data/08_reporting/temporal_linear_wavg/train --params-path params.yaml 
+      --params-key train_temporal_linear_wavg
+    deps:
+    - path: data/05_model_input/train
+      hash: md5
+      md5: 840f6c912a4c6ae3103627dd32594242.dir
+      size: 843667150
+      nfiles: 34039
+    - path: data/06_models/temporal_linear_wavg
+      hash: md5
+      md5: d8963d9ef59a5ef56448ed72c5c34511.dir
+      size: 103811952
+      nfiles: 5
+    - path: scripts/evaluate_ablation_global.py
+      hash: md5
+      md5: 6d3be966e0bf9143aa20ba3228a33c67
+      size: 5251
+    - path: src/tube_multiscale_fusion/ablation_classifier.py
+      hash: md5
+      md5: 1c4f4f7de41e3253d389d3f4314fc4d0
+      size: 8701
+    - path: src/tube_multiscale_fusion/dataset.py
+      hash: md5
+      md5: d7d3e37c27fd25f7612751b3c5869dd2
+      size: 3036
+    - path: src/tube_multiscale_fusion/eval_plots.py
+      hash: md5
+      md5: f76a38b124a936129d9e0cc6910b9e33
+      size: 3399
+    - path: src/tube_multiscale_fusion/global_branch.py
+      hash: md5
+      md5: 22bcbb284170752ff61a781a6119a474
+      size: 11333
+    - path: src/tube_multiscale_fusion/lit_ablation.py
+      hash: md5
+      md5: e09db331d78b94f700a33fadbdedd246
+      size: 13267
+    params:
+      params.yaml:
+        global_branch:
+          backbone: vit_small_patch14_dinov2.lvd142m
+          aggregator:
+            num_layers: 2
+            num_heads: 6
+            ffn_dim: 1536
+            dropout: 0.1
+        train_temporal_linear_wavg:
+          max_frames: 16
+          batch_size: 16
+          num_workers: 4
+          learning_rate: 0.0001
+          weight_decay: 0.05
+          max_epochs: 30
+          early_stop_patience: 5
+          seed: 42
+          head_hidden_dim: 256
+          head_dropout: 0.1
+          use_cosine_warmup: true
+          warmup_frac: 0.05
+          img_size: 224
+          ablation_variant: no_spatial
+          finetune: true
+          finetune_last_n_blocks: 1
+          backbone_lr: 1e-05
+          aggregator_kind: linear_wavg
+    outs:
+    - path: data/08_reporting/temporal_linear_wavg/train
+      hash: md5
+      md5: 548e7b934bc97ee99dfe136fab89770e.dir
+      size: 584221
+      nfiles: 6
+  evaluate_temporal@mlp-train:
+    cmd: uv run python scripts/evaluate_ablation_global.py --data-dir 
+      data/05_model_input/train --checkpoint 
+      data/06_models/temporal_mlp/best_checkpoint.pt --output-dir 
+      data/08_reporting/temporal_mlp/train --params-path params.yaml 
+      --params-key train_temporal_mlp
+    deps:
+    - path: data/05_model_input/train
+      hash: md5
+      md5: 840f6c912a4c6ae3103627dd32594242.dir
+      size: 843667150
+      nfiles: 34039
+    - path: data/06_models/temporal_mlp
+      hash: md5
+      md5: 9a4eec86025bd02e640b39aedfc7b252.dir
+      size: 182282337
+      nfiles: 5
+    - path: scripts/evaluate_ablation_global.py
+      hash: md5
+      md5: 6d3be966e0bf9143aa20ba3228a33c67
+      size: 5251
+    - path: src/tube_multiscale_fusion/ablation_classifier.py
+      hash: md5
+      md5: 1c4f4f7de41e3253d389d3f4314fc4d0
+      size: 8701
+    - path: src/tube_multiscale_fusion/dataset.py
+      hash: md5
+      md5: d7d3e37c27fd25f7612751b3c5869dd2
+      size: 3036
+    - path: src/tube_multiscale_fusion/eval_plots.py
+      hash: md5
+      md5: f76a38b124a936129d9e0cc6910b9e33
+      size: 3399
+    - path: src/tube_multiscale_fusion/global_branch.py
+      hash: md5
+      md5: 22bcbb284170752ff61a781a6119a474
+      size: 11333
+    - path: src/tube_multiscale_fusion/lit_ablation.py
+      hash: md5
+      md5: e09db331d78b94f700a33fadbdedd246
+      size: 13267
+    params:
+      params.yaml:
+        global_branch:
+          backbone: vit_small_patch14_dinov2.lvd142m
+          aggregator:
+            num_layers: 2
+            num_heads: 6
+            ffn_dim: 1536
+            dropout: 0.1
+        train_temporal_mlp:
+          max_frames: 16
+          batch_size: 16
+          num_workers: 4
+          learning_rate: 0.0001
+          weight_decay: 0.05
+          max_epochs: 30
+          early_stop_patience: 5
+          seed: 42
+          head_hidden_dim: 256
+          head_dropout: 0.1
+          use_cosine_warmup: true
+          warmup_frac: 0.05
+          img_size: 224
+          ablation_variant: no_spatial
+          finetune: true
+          finetune_last_n_blocks: 1
+          backbone_lr: 1e-05
+          aggregator_kind: mlp
+    outs:
+    - path: data/08_reporting/temporal_mlp/train
+      hash: md5
+      md5: 0da9cb59981b9a125b7ef631e62a7fee.dir
+      size: 578771
+      nfiles: 6
+  evaluate_temporal@mlp-val:
+    cmd: uv run python scripts/evaluate_ablation_global.py --data-dir 
+      data/05_model_input/val --checkpoint 
+      data/06_models/temporal_mlp/best_checkpoint.pt --output-dir 
+      data/08_reporting/temporal_mlp/val --params-path params.yaml --params-key 
+      train_temporal_mlp
+    deps:
+    - path: data/05_model_input/val
+      hash: md5
+      md5: f8585e64f607a7a8d7d1651a9cbc930e.dir
+      size: 77725173
+      nfiles: 3219
+    - path: data/06_models/temporal_mlp
+      hash: md5
+      md5: 9a4eec86025bd02e640b39aedfc7b252.dir
+      size: 182282337
+      nfiles: 5
+    - path: scripts/evaluate_ablation_global.py
+      hash: md5
+      md5: 6d3be966e0bf9143aa20ba3228a33c67
+      size: 5251
+    - path: src/tube_multiscale_fusion/ablation_classifier.py
+      hash: md5
+      md5: 1c4f4f7de41e3253d389d3f4314fc4d0
+      size: 8701
+    - path: src/tube_multiscale_fusion/dataset.py
+      hash: md5
+      md5: d7d3e37c27fd25f7612751b3c5869dd2
+      size: 3036
+    - path: src/tube_multiscale_fusion/eval_plots.py
+      hash: md5
+      md5: f76a38b124a936129d9e0cc6910b9e33
+      size: 3399
+    - path: src/tube_multiscale_fusion/global_branch.py
+      hash: md5
+      md5: 22bcbb284170752ff61a781a6119a474
+      size: 11333
+    - path: src/tube_multiscale_fusion/lit_ablation.py
+      hash: md5
+      md5: e09db331d78b94f700a33fadbdedd246
+      size: 13267
+    params:
+      params.yaml:
+        global_branch:
+          backbone: vit_small_patch14_dinov2.lvd142m
+          aggregator:
+            num_layers: 2
+            num_heads: 6
+            ffn_dim: 1536
+            dropout: 0.1
+        train_temporal_mlp:
+          max_frames: 16
+          batch_size: 16
+          num_workers: 4
+          learning_rate: 0.0001
+          weight_decay: 0.05
+          max_epochs: 30
+          early_stop_patience: 5
+          seed: 42
+          head_hidden_dim: 256
+          head_dropout: 0.1
+          use_cosine_warmup: true
+          warmup_frac: 0.05
+          img_size: 224
+          ablation_variant: no_spatial
+          finetune: true
+          finetune_last_n_blocks: 1
+          backbone_lr: 1e-05
+          aggregator_kind: mlp
+    outs:
+    - path: data/08_reporting/temporal_mlp/val
+      hash: md5
+      md5: 83dca1e4e32f2c46e3db807da84cb1a2.dir
+      size: 141485
+      nfiles: 6
+  train_spatial@tsm:
+    cmd: uv run python scripts/train_ablation.py --train-dir 
+      data/05_model_input/train --val-dir data/05_model_input/val --output-dir 
+      data/06_models/spatial_tsm --params-path params.yaml --params-key 
+      train_spatial_tsm
+    deps:
+    - path: data/05_model_input/train
+      hash: md5
+      md5: 840f6c912a4c6ae3103627dd32594242.dir
+      size: 843667150
+      nfiles: 34039
+    - path: data/05_model_input/val
+      hash: md5
+      md5: f8585e64f607a7a8d7d1651a9cbc930e.dir
+      size: 77725173
+      nfiles: 3219
+    - path: scripts/train_ablation.py
+      hash: md5
+      md5: 4183862a7b58d1a33f4a49f30bdc2d92
+      size: 4858
+    - path: src/tube_multiscale_fusion/ablation_classifier.py
+      hash: md5
+      md5: d3feb508e850de3db730be7b10564d3a
+      size: 8872
+    - path: src/tube_multiscale_fusion/augment.py
+      hash: md5
+      md5: f52a42f92caa2fac859bf3a81a18971d
+      size: 9489
+    - path: src/tube_multiscale_fusion/dataset.py
+      hash: md5
+      md5: d7d3e37c27fd25f7612751b3c5869dd2
+      size: 3036
+    - path: src/tube_multiscale_fusion/fusion.py
+      hash: md5
+      md5: 695dd2af05cb3a47cc3387c9c4ba210d
+      size: 7308
+    - path: src/tube_multiscale_fusion/lit_ablation.py
+      hash: md5
+      md5: 76a76ab01092ab2c2215724c9c0155e7
+      size: 13357
+    - path: src/tube_multiscale_fusion/local_branch.py
+      hash: md5
+      md5: 6fddb4c6db1fcb59c61261bed915b693
+      size: 19137
+    params:
+      params.yaml:
+        augment:
+          enabled: true
+          spatial:
+            flip_prob: 0.5
+            rotation_deg: 5.0
+            scale_range:
+            - 0.9
+            - 1.1
+            translate_frac: 0.05
+          photometric:
+            brightness_range:
+            - 0.8
+            - 1.2
+            contrast_range:
+            - 0.8
+            - 1.2
+            saturation_range:
+            - 0.8
+            - 1.2
+          temporal:
+            subseq_prob: 0.5
+            subseq_min_len: 4
+            stride_prob: 0.25
+            frame_drop_prob: 0.15
+            min_valid_after_drop: 4
+        fusion:
+          d_fusion: 384
+          num_layers: 2
+          num_heads: 6
+          ffn_dim: 1536
+          dropout: 0.1
+        local_branch:
+          grid_size: 2
+          cell_size: 112
+          tube_length: 4
+          temporal_stride: 2
+          encoder:
+            t_kernel: 2
+            h_patch: 14
+            w_patch: 14
+            embed_dim: 384
+            num_layers: 2
+            num_heads: 6
+            ffn_dim: 1536
+            dropout: 0.1
+        train_spatial_tsm:
+          max_frames: 16
+          batch_size: 16
+          num_workers: 4
+          learning_rate: 0.0001
+          weight_decay: 0.05
+          max_epochs: 30
+          early_stop_patience: 5
+          seed: 42
+          head_hidden_dim: 256
+          head_dropout: 0.1
+          use_cosine_warmup: true
+          warmup_frac: 0.05
+          img_size: 224
+          ablation_variant: no_temporal
+          local_branch_section: local_branch
+          query_dim: 384
+          encoder_kind: tsm
+    outs:
+    - path: data/06_models/spatial_tsm
+      hash: md5
+      md5: 3a18c01dad1a009114e8d9431eefd902.dir
+      size: 92160971
+      nfiles: 5
+  train_spatial@convlstm:
+    cmd: uv run python scripts/train_ablation.py --train-dir 
+      data/05_model_input/train --val-dir data/05_model_input/val --output-dir 
+      data/06_models/spatial_convlstm --params-path params.yaml --params-key 
+      train_spatial_convlstm
+    deps:
+    - path: data/05_model_input/train
+      hash: md5
+      md5: 840f6c912a4c6ae3103627dd32594242.dir
+      size: 843667150
+      nfiles: 34039
+    - path: data/05_model_input/val
+      hash: md5
+      md5: f8585e64f607a7a8d7d1651a9cbc930e.dir
+      size: 77725173
+      nfiles: 3219
+    - path: scripts/train_ablation.py
+      hash: md5
+      md5: 4183862a7b58d1a33f4a49f30bdc2d92
+      size: 4858
+    - path: src/tube_multiscale_fusion/ablation_classifier.py
+      hash: md5
+      md5: d3feb508e850de3db730be7b10564d3a
+      size: 8872
+    - path: src/tube_multiscale_fusion/augment.py
+      hash: md5
+      md5: f52a42f92caa2fac859bf3a81a18971d
+      size: 9489
+    - path: src/tube_multiscale_fusion/dataset.py
+      hash: md5
+      md5: d7d3e37c27fd25f7612751b3c5869dd2
+      size: 3036
+    - path: src/tube_multiscale_fusion/fusion.py
+      hash: md5
+      md5: 695dd2af05cb3a47cc3387c9c4ba210d
+      size: 7308
+    - path: src/tube_multiscale_fusion/lit_ablation.py
+      hash: md5
+      md5: 76a76ab01092ab2c2215724c9c0155e7
+      size: 13357
+    - path: src/tube_multiscale_fusion/local_branch.py
+      hash: md5
+      md5: 6fddb4c6db1fcb59c61261bed915b693
+      size: 19137
+    params:
+      params.yaml:
+        augment:
+          enabled: true
+          spatial:
+            flip_prob: 0.5
+            rotation_deg: 5.0
+            scale_range:
+            - 0.9
+            - 1.1
+            translate_frac: 0.05
+          photometric:
+            brightness_range:
+            - 0.8
+            - 1.2
+            contrast_range:
+            - 0.8
+            - 1.2
+            saturation_range:
+            - 0.8
+            - 1.2
+          temporal:
+            subseq_prob: 0.5
+            subseq_min_len: 4
+            stride_prob: 0.25
+            frame_drop_prob: 0.15
+            min_valid_after_drop: 4
+        fusion:
+          d_fusion: 384
+          num_layers: 2
+          num_heads: 6
+          ffn_dim: 1536
+          dropout: 0.1
+        local_branch:
+          grid_size: 2
+          cell_size: 112
+          tube_length: 4
+          temporal_stride: 2
+          encoder:
+            t_kernel: 2
+            h_patch: 14
+            w_patch: 14
+            embed_dim: 384
+            num_layers: 2
+            num_heads: 6
+            ffn_dim: 1536
+            dropout: 0.1
+        train_spatial_convlstm:
+          max_frames: 16
+          batch_size: 16
+          num_workers: 4
+          learning_rate: 0.0001
+          weight_decay: 0.05
+          max_epochs: 30
+          early_stop_patience: 5
+          seed: 42
+          head_hidden_dim: 256
+          head_dropout: 0.1
+          use_cosine_warmup: true
+          warmup_frac: 0.05
+          img_size: 224
+          ablation_variant: no_temporal
+          local_branch_section: local_branch
+          query_dim: 384
+          encoder_kind: convlstm
+    outs:
+    - path: data/06_models/spatial_convlstm
+      hash: md5
+      md5: fb715017498a5656597aef62fe5b8b9e.dir
+      size: 103970301
+      nfiles: 5
+  evaluate_spatial@tsm-val:
+    cmd: uv run python scripts/evaluate_ablation.py --data-dir 
+      data/05_model_input/val --checkpoint 
+      data/06_models/spatial_tsm/best_checkpoint.pt --output-dir 
+      data/08_reporting/spatial_tsm/val --params-path params.yaml --params-key 
+      train_spatial_tsm
+    deps:
+    - path: data/05_model_input/val
+      hash: md5
+      md5: f8585e64f607a7a8d7d1651a9cbc930e.dir
+      size: 77725173
+      nfiles: 3219
+    - path: data/06_models/spatial_tsm
+      hash: md5
+      md5: 3a18c01dad1a009114e8d9431eefd902.dir
+      size: 92160971
+      nfiles: 5
+    - path: scripts/evaluate_ablation.py
+      hash: md5
+      md5: c3c9a3d0195471eb287ebd4a21b4321e
+      size: 5231
+    - path: src/tube_multiscale_fusion/ablation_classifier.py
+      hash: md5
+      md5: d3feb508e850de3db730be7b10564d3a
+      size: 8872
+    - path: src/tube_multiscale_fusion/dataset.py
+      hash: md5
+      md5: d7d3e37c27fd25f7612751b3c5869dd2
+      size: 3036
+    - path: src/tube_multiscale_fusion/eval_plots.py
+      hash: md5
+      md5: f76a38b124a936129d9e0cc6910b9e33
+      size: 3399
+    - path: src/tube_multiscale_fusion/fusion.py
+      hash: md5
+      md5: 695dd2af05cb3a47cc3387c9c4ba210d
+      size: 7308
+    - path: src/tube_multiscale_fusion/lit_ablation.py
+      hash: md5
+      md5: 76a76ab01092ab2c2215724c9c0155e7
+      size: 13357
+    - path: src/tube_multiscale_fusion/local_branch.py
+      hash: md5
+      md5: 6fddb4c6db1fcb59c61261bed915b693
+      size: 19137
+    params:
+      params.yaml:
+        fusion:
+          d_fusion: 384
+          num_layers: 2
+          num_heads: 6
+          ffn_dim: 1536
+          dropout: 0.1
+        local_branch:
+          grid_size: 2
+          cell_size: 112
+          tube_length: 4
+          temporal_stride: 2
+          encoder:
+            t_kernel: 2
+            h_patch: 14
+            w_patch: 14
+            embed_dim: 384
+            num_layers: 2
+            num_heads: 6
+            ffn_dim: 1536
+            dropout: 0.1
+        train_spatial_tsm:
+          max_frames: 16
+          batch_size: 16
+          num_workers: 4
+          learning_rate: 0.0001
+          weight_decay: 0.05
+          max_epochs: 30
+          early_stop_patience: 5
+          seed: 42
+          head_hidden_dim: 256
+          head_dropout: 0.1
+          use_cosine_warmup: true
+          warmup_frac: 0.05
+          img_size: 224
+          ablation_variant: no_temporal
+          local_branch_section: local_branch
+          query_dim: 384
+          encoder_kind: tsm
+    outs:
+    - path: data/08_reporting/spatial_tsm/val
+      hash: md5
+      md5: a42eb9d0ba7a2cdbe019654f0b859f54.dir
+      size: 152333
+      nfiles: 6
+  train_spatial@resnet3d:
+    cmd: uv run python scripts/train_ablation.py --train-dir 
+      data/05_model_input/train --val-dir data/05_model_input/val --output-dir 
+      data/06_models/spatial_resnet3d --params-path params.yaml --params-key 
+      train_spatial_resnet3d
+    deps:
+    - path: data/05_model_input/train
+      hash: md5
+      md5: 840f6c912a4c6ae3103627dd32594242.dir
+      size: 843667150
+      nfiles: 34039
+    - path: data/05_model_input/val
+      hash: md5
+      md5: f8585e64f607a7a8d7d1651a9cbc930e.dir
+      size: 77725173
+      nfiles: 3219
+    - path: scripts/train_ablation.py
+      hash: md5
+      md5: 4183862a7b58d1a33f4a49f30bdc2d92
+      size: 4858
+    - path: src/tube_multiscale_fusion/ablation_classifier.py
+      hash: md5
+      md5: d3feb508e850de3db730be7b10564d3a
+      size: 8872
+    - path: src/tube_multiscale_fusion/augment.py
+      hash: md5
+      md5: f52a42f92caa2fac859bf3a81a18971d
+      size: 9489
+    - path: src/tube_multiscale_fusion/dataset.py
+      hash: md5
+      md5: d7d3e37c27fd25f7612751b3c5869dd2
+      size: 3036
+    - path: src/tube_multiscale_fusion/fusion.py
+      hash: md5
+      md5: 695dd2af05cb3a47cc3387c9c4ba210d
+      size: 7308
+    - path: src/tube_multiscale_fusion/lit_ablation.py
+      hash: md5
+      md5: 76a76ab01092ab2c2215724c9c0155e7
+      size: 13357
+    - path: src/tube_multiscale_fusion/local_branch.py
+      hash: md5
+      md5: 6fddb4c6db1fcb59c61261bed915b693
+      size: 19137
+    params:
+      params.yaml:
+        augment:
+          enabled: true
+          spatial:
+            flip_prob: 0.5
+            rotation_deg: 5.0
+            scale_range:
+            - 0.9
+            - 1.1
+            translate_frac: 0.05
+          photometric:
+            brightness_range:
+            - 0.8
+            - 1.2
+            contrast_range:
+            - 0.8
+            - 1.2
+            saturation_range:
+            - 0.8
+            - 1.2
+          temporal:
+            subseq_prob: 0.5
+            subseq_min_len: 4
+            stride_prob: 0.25
+            frame_drop_prob: 0.15
+            min_valid_after_drop: 4
+        fusion:
+          d_fusion: 384
+          num_layers: 2
+          num_heads: 6
+          ffn_dim: 1536
+          dropout: 0.1
+        local_branch:
+          grid_size: 2
+          cell_size: 112
+          tube_length: 4
+          temporal_stride: 2
+          encoder:
+            t_kernel: 2
+            h_patch: 14
+            w_patch: 14
+            embed_dim: 384
+            num_layers: 2
+            num_heads: 6
+            ffn_dim: 1536
+            dropout: 0.1
+        train_spatial_resnet3d:
+          max_frames: 16
+          batch_size: 8
+          num_workers: 4
+          learning_rate: 0.0001
+          weight_decay: 0.05
+          max_epochs: 30
+          early_stop_patience: 5
+          seed: 42
+          head_hidden_dim: 256
+          head_dropout: 0.1
+          use_cosine_warmup: true
+          warmup_frac: 0.05
+          img_size: 224
+          ablation_variant: no_temporal
+          local_branch_section: local_branch
+          query_dim: 384
+          encoder_kind: resnet3d
+    outs:
+    - path: data/06_models/spatial_resnet3d
+      hash: md5
+      md5: dd66eec446fbc17992fc4e162546dcf2.dir
+      size: 487058223
+      nfiles: 5
+  evaluate_spatial@resnet3d-val:
+    cmd: uv run python scripts/evaluate_ablation.py --data-dir 
+      data/05_model_input/val --checkpoint 
+      data/06_models/spatial_resnet3d/best_checkpoint.pt --output-dir 
+      data/08_reporting/spatial_resnet3d/val --params-path params.yaml 
+      --params-key train_spatial_resnet3d
+    deps:
+    - path: data/05_model_input/val
+      hash: md5
+      md5: f8585e64f607a7a8d7d1651a9cbc930e.dir
+      size: 77725173
+      nfiles: 3219
+    - path: data/06_models/spatial_resnet3d
+      hash: md5
+      md5: dd66eec446fbc17992fc4e162546dcf2.dir
+      size: 487058223
+      nfiles: 5
+    - path: scripts/evaluate_ablation.py
+      hash: md5
+      md5: c3c9a3d0195471eb287ebd4a21b4321e
+      size: 5231
+    - path: src/tube_multiscale_fusion/ablation_classifier.py
+      hash: md5
+      md5: d3feb508e850de3db730be7b10564d3a
+      size: 8872
+    - path: src/tube_multiscale_fusion/dataset.py
+      hash: md5
+      md5: d7d3e37c27fd25f7612751b3c5869dd2
+      size: 3036
+    - path: src/tube_multiscale_fusion/eval_plots.py
+      hash: md5
+      md5: f76a38b124a936129d9e0cc6910b9e33
+      size: 3399
+    - path: src/tube_multiscale_fusion/fusion.py
+      hash: md5
+      md5: 695dd2af05cb3a47cc3387c9c4ba210d
+      size: 7308
+    - path: src/tube_multiscale_fusion/lit_ablation.py
+      hash: md5
+      md5: 76a76ab01092ab2c2215724c9c0155e7
+      size: 13357
+    - path: src/tube_multiscale_fusion/local_branch.py
+      hash: md5
+      md5: 6fddb4c6db1fcb59c61261bed915b693
+      size: 19137
+    params:
+      params.yaml:
+        fusion:
+          d_fusion: 384
+          num_layers: 2
+          num_heads: 6
+          ffn_dim: 1536
+          dropout: 0.1
+        local_branch:
+          grid_size: 2
+          cell_size: 112
+          tube_length: 4
+          temporal_stride: 2
+          encoder:
+            t_kernel: 2
+            h_patch: 14
+            w_patch: 14
+            embed_dim: 384
+            num_layers: 2
+            num_heads: 6
+            ffn_dim: 1536
+            dropout: 0.1
+        train_spatial_resnet3d:
+          max_frames: 16
+          batch_size: 8
+          num_workers: 4
+          learning_rate: 0.0001
+          weight_decay: 0.05
+          max_epochs: 30
+          early_stop_patience: 5
+          seed: 42
+          head_hidden_dim: 256
+          head_dropout: 0.1
+          use_cosine_warmup: true
+          warmup_frac: 0.05
+          img_size: 224
+          ablation_variant: no_temporal
+          local_branch_section: local_branch
+          query_dim: 384
+          encoder_kind: resnet3d
+    outs:
+    - path: data/08_reporting/spatial_resnet3d/val
+      hash: md5
+      md5: ddf6582cc94ace1363b6c39503c78446.dir
+      size: 145778
+      nfiles: 6
+  train_spatial@vivit:
+    cmd: uv run python scripts/train_ablation.py --train-dir 
+      data/05_model_input/train --val-dir data/05_model_input/val --output-dir 
+      data/06_models/spatial_vivit --params-path params.yaml --params-key 
+      train_spatial_vivit
+    deps:
+    - path: data/05_model_input/train
+      hash: md5
+      md5: 840f6c912a4c6ae3103627dd32594242.dir
+      size: 843667150
+      nfiles: 34039
+    - path: data/05_model_input/val
+      hash: md5
+      md5: f8585e64f607a7a8d7d1651a9cbc930e.dir
+      size: 77725173
+      nfiles: 3219
+    - path: scripts/train_ablation.py
+      hash: md5
+      md5: 4183862a7b58d1a33f4a49f30bdc2d92
+      size: 4858
+    - path: src/tube_multiscale_fusion/ablation_classifier.py
+      hash: md5
+      md5: d3feb508e850de3db730be7b10564d3a
+      size: 8872
+    - path: src/tube_multiscale_fusion/augment.py
+      hash: md5
+      md5: f52a42f92caa2fac859bf3a81a18971d
+      size: 9489
+    - path: src/tube_multiscale_fusion/dataset.py
+      hash: md5
+      md5: d7d3e37c27fd25f7612751b3c5869dd2
+      size: 3036
+    - path: src/tube_multiscale_fusion/fusion.py
+      hash: md5
+      md5: 695dd2af05cb3a47cc3387c9c4ba210d
+      size: 7308
+    - path: src/tube_multiscale_fusion/lit_ablation.py
+      hash: md5
+      md5: 76a76ab01092ab2c2215724c9c0155e7
+      size: 13357
+    - path: src/tube_multiscale_fusion/local_branch.py
+      hash: md5
+      md5: 6fddb4c6db1fcb59c61261bed915b693
+      size: 19137
+    params:
+      params.yaml:
+        augment:
+          enabled: true
+          spatial:
+            flip_prob: 0.5
+            rotation_deg: 5.0
+            scale_range:
+            - 0.9
+            - 1.1
+            translate_frac: 0.05
+          photometric:
+            brightness_range:
+            - 0.8
+            - 1.2
+            contrast_range:
+            - 0.8
+            - 1.2
+            saturation_range:
+            - 0.8
+            - 1.2
+          temporal:
+            subseq_prob: 0.5
+            subseq_min_len: 4
+            stride_prob: 0.25
+            frame_drop_prob: 0.15
+            min_valid_after_drop: 4
+        fusion:
+          d_fusion: 384
+          num_layers: 2
+          num_heads: 6
+          ffn_dim: 1536
+          dropout: 0.1
+        local_branch:
+          grid_size: 2
+          cell_size: 112
+          tube_length: 4
+          temporal_stride: 2
+          encoder:
+            t_kernel: 2
+            h_patch: 14
+            w_patch: 14
+            embed_dim: 384
+            num_layers: 2
+            num_heads: 6
+            ffn_dim: 1536
+            dropout: 0.1
+        train_spatial_vivit:
+          max_frames: 16
+          batch_size: 16
+          num_workers: 4
+          learning_rate: 0.0001
+          weight_decay: 0.05
+          max_epochs: 30
+          early_stop_patience: 5
+          seed: 42
+          head_hidden_dim: 256
+          head_dropout: 0.1
+          use_cosine_warmup: true
+          warmup_frac: 0.05
+          img_size: 224
+          ablation_variant: no_temporal
+          local_branch_section: local_branch
+          query_dim: 384
+          encoder_kind: vivit
+    outs:
+    - path: data/06_models/spatial_vivit
+      hash: md5
+      md5: 54028b5520975a36c70ad309d423317d.dir
+      size: 177496976
+      nfiles: 5
+  evaluate_spatial@vivit-train:
+    cmd: uv run python scripts/evaluate_ablation.py --data-dir 
+      data/05_model_input/train --checkpoint 
+      data/06_models/spatial_vivit/best_checkpoint.pt --output-dir 
+      data/08_reporting/spatial_vivit/train --params-path params.yaml 
+      --params-key train_spatial_vivit
+    deps:
+    - path: data/05_model_input/train
+      hash: md5
+      md5: 840f6c912a4c6ae3103627dd32594242.dir
+      size: 843667150
+      nfiles: 34039
+    - path: data/06_models/spatial_vivit
+      hash: md5
+      md5: 54028b5520975a36c70ad309d423317d.dir
+      size: 177496976
+      nfiles: 5
+    - path: scripts/evaluate_ablation.py
+      hash: md5
+      md5: c3c9a3d0195471eb287ebd4a21b4321e
+      size: 5231
+    - path: src/tube_multiscale_fusion/ablation_classifier.py
+      hash: md5
+      md5: d3feb508e850de3db730be7b10564d3a
+      size: 8872
+    - path: src/tube_multiscale_fusion/dataset.py
+      hash: md5
+      md5: d7d3e37c27fd25f7612751b3c5869dd2
+      size: 3036
+    - path: src/tube_multiscale_fusion/eval_plots.py
+      hash: md5
+      md5: f76a38b124a936129d9e0cc6910b9e33
+      size: 3399
+    - path: src/tube_multiscale_fusion/fusion.py
+      hash: md5
+      md5: 695dd2af05cb3a47cc3387c9c4ba210d
+      size: 7308
+    - path: src/tube_multiscale_fusion/lit_ablation.py
+      hash: md5
+      md5: 76a76ab01092ab2c2215724c9c0155e7
+      size: 13357
+    - path: src/tube_multiscale_fusion/local_branch.py
+      hash: md5
+      md5: 6fddb4c6db1fcb59c61261bed915b693
+      size: 19137
+    params:
+      params.yaml:
+        fusion:
+          d_fusion: 384
+          num_layers: 2
+          num_heads: 6
+          ffn_dim: 1536
+          dropout: 0.1
+        local_branch:
+          grid_size: 2
+          cell_size: 112
+          tube_length: 4
+          temporal_stride: 2
+          encoder:
+            t_kernel: 2
+            h_patch: 14
+            w_patch: 14
+            embed_dim: 384
+            num_layers: 2
+            num_heads: 6
+            ffn_dim: 1536
+            dropout: 0.1
+        train_spatial_vivit:
+          max_frames: 16
+          batch_size: 16
+          num_workers: 4
+          learning_rate: 0.0001
+          weight_decay: 0.05
+          max_epochs: 30
+          early_stop_patience: 5
+          seed: 42
+          head_hidden_dim: 256
+          head_dropout: 0.1
+          use_cosine_warmup: true
+          warmup_frac: 0.05
+          img_size: 224
+          ablation_variant: no_temporal
+          local_branch_section: local_branch
+          query_dim: 384
+          encoder_kind: vivit
+    outs:
+    - path: data/08_reporting/spatial_vivit/train
+      hash: md5
+      md5: 632d9f4c3da97a8deb9bef161de4624a.dir
+      size: 588770
+      nfiles: 6
+  evaluate_spatial@convlstm-train:
+    cmd: uv run python scripts/evaluate_ablation.py --data-dir 
+      data/05_model_input/train --checkpoint 
+      data/06_models/spatial_convlstm/best_checkpoint.pt --output-dir 
+      data/08_reporting/spatial_convlstm/train --params-path params.yaml 
+      --params-key train_spatial_convlstm
+    deps:
+    - path: data/05_model_input/train
+      hash: md5
+      md5: 840f6c912a4c6ae3103627dd32594242.dir
+      size: 843667150
+      nfiles: 34039
+    - path: data/06_models/spatial_convlstm
+      hash: md5
+      md5: fb715017498a5656597aef62fe5b8b9e.dir
+      size: 103970301
+      nfiles: 5
+    - path: scripts/evaluate_ablation.py
+      hash: md5
+      md5: c3c9a3d0195471eb287ebd4a21b4321e
+      size: 5231
+    - path: src/tube_multiscale_fusion/ablation_classifier.py
+      hash: md5
+      md5: d3feb508e850de3db730be7b10564d3a
+      size: 8872
+    - path: src/tube_multiscale_fusion/dataset.py
+      hash: md5
+      md5: d7d3e37c27fd25f7612751b3c5869dd2
+      size: 3036
+    - path: src/tube_multiscale_fusion/eval_plots.py
+      hash: md5
+      md5: f76a38b124a936129d9e0cc6910b9e33
+      size: 3399
+    - path: src/tube_multiscale_fusion/fusion.py
+      hash: md5
+      md5: 695dd2af05cb3a47cc3387c9c4ba210d
+      size: 7308
+    - path: src/tube_multiscale_fusion/lit_ablation.py
+      hash: md5
+      md5: 76a76ab01092ab2c2215724c9c0155e7
+      size: 13357
+    - path: src/tube_multiscale_fusion/local_branch.py
+      hash: md5
+      md5: 6fddb4c6db1fcb59c61261bed915b693
+      size: 19137
+    params:
+      params.yaml:
+        fusion:
+          d_fusion: 384
+          num_layers: 2
+          num_heads: 6
+          ffn_dim: 1536
+          dropout: 0.1
+        local_branch:
+          grid_size: 2
+          cell_size: 112
+          tube_length: 4
+          temporal_stride: 2
+          encoder:
+            t_kernel: 2
+            h_patch: 14
+            w_patch: 14
+            embed_dim: 384
+            num_layers: 2
+            num_heads: 6
+            ffn_dim: 1536
+            dropout: 0.1
+        train_spatial_convlstm:
+          max_frames: 16
+          batch_size: 16
+          num_workers: 4
+          learning_rate: 0.0001
+          weight_decay: 0.05
+          max_epochs: 30
+          early_stop_patience: 5
+          seed: 42
+          head_hidden_dim: 256
+          head_dropout: 0.1
+          use_cosine_warmup: true
+          warmup_frac: 0.05
+          img_size: 224
+          ablation_variant: no_temporal
+          local_branch_section: local_branch
+          query_dim: 384
+          encoder_kind: convlstm
+    outs:
+    - path: data/08_reporting/spatial_convlstm/train
+      hash: md5
+      md5: 5479a2406a060866fa7cfd992e60efcd.dir
+      size: 587399
+      nfiles: 6
+  evaluate_spatial@convlstm-val:
+    cmd: uv run python scripts/evaluate_ablation.py --data-dir 
+      data/05_model_input/val --checkpoint 
+      data/06_models/spatial_convlstm/best_checkpoint.pt --output-dir 
+      data/08_reporting/spatial_convlstm/val --params-path params.yaml 
+      --params-key train_spatial_convlstm
+    deps:
+    - path: data/05_model_input/val
+      hash: md5
+      md5: f8585e64f607a7a8d7d1651a9cbc930e.dir
+      size: 77725173
+      nfiles: 3219
+    - path: data/06_models/spatial_convlstm
+      hash: md5
+      md5: fb715017498a5656597aef62fe5b8b9e.dir
+      size: 103970301
+      nfiles: 5
+    - path: scripts/evaluate_ablation.py
+      hash: md5
+      md5: c3c9a3d0195471eb287ebd4a21b4321e
+      size: 5231
+    - path: src/tube_multiscale_fusion/ablation_classifier.py
+      hash: md5
+      md5: d3feb508e850de3db730be7b10564d3a
+      size: 8872
+    - path: src/tube_multiscale_fusion/dataset.py
+      hash: md5
+      md5: d7d3e37c27fd25f7612751b3c5869dd2
+      size: 3036
+    - path: src/tube_multiscale_fusion/eval_plots.py
+      hash: md5
+      md5: f76a38b124a936129d9e0cc6910b9e33
+      size: 3399
+    - path: src/tube_multiscale_fusion/fusion.py
+      hash: md5
+      md5: 695dd2af05cb3a47cc3387c9c4ba210d
+      size: 7308
+    - path: src/tube_multiscale_fusion/lit_ablation.py
+      hash: md5
+      md5: 76a76ab01092ab2c2215724c9c0155e7
+      size: 13357
+    - path: src/tube_multiscale_fusion/local_branch.py
+      hash: md5
+      md5: 6fddb4c6db1fcb59c61261bed915b693
+      size: 19137
+    params:
+      params.yaml:
+        fusion:
+          d_fusion: 384
+          num_layers: 2
+          num_heads: 6
+          ffn_dim: 1536
+          dropout: 0.1
+        local_branch:
+          grid_size: 2
+          cell_size: 112
+          tube_length: 4
+          temporal_stride: 2
+          encoder:
+            t_kernel: 2
+            h_patch: 14
+            w_patch: 14
+            embed_dim: 384
+            num_layers: 2
+            num_heads: 6
+            ffn_dim: 1536
+            dropout: 0.1
+        train_spatial_convlstm:
+          max_frames: 16
+          batch_size: 16
+          num_workers: 4
+          learning_rate: 0.0001
+          weight_decay: 0.05
+          max_epochs: 30
+          early_stop_patience: 5
+          seed: 42
+          head_hidden_dim: 256
+          head_dropout: 0.1
+          use_cosine_warmup: true
+          warmup_frac: 0.05
+          img_size: 224
+          ablation_variant: no_temporal
+          local_branch_section: local_branch
+          query_dim: 384
+          encoder_kind: convlstm
+    outs:
+    - path: data/08_reporting/spatial_convlstm/val
+      hash: md5
+      md5: 1573f06f3500a52a95e484fb62451c24.dir
+      size: 149608
+      nfiles: 6
+  evaluate_spatial@tsm-train:
+    cmd: uv run python scripts/evaluate_ablation.py --data-dir 
+      data/05_model_input/train --checkpoint 
+      data/06_models/spatial_tsm/best_checkpoint.pt --output-dir 
+      data/08_reporting/spatial_tsm/train --params-path params.yaml --params-key
+      train_spatial_tsm
+    deps:
+    - path: data/05_model_input/train
+      hash: md5
+      md5: 840f6c912a4c6ae3103627dd32594242.dir
+      size: 843667150
+      nfiles: 34039
+    - path: data/06_models/spatial_tsm
+      hash: md5
+      md5: 3a18c01dad1a009114e8d9431eefd902.dir
+      size: 92160971
+      nfiles: 5
+    - path: scripts/evaluate_ablation.py
+      hash: md5
+      md5: c3c9a3d0195471eb287ebd4a21b4321e
+      size: 5231
+    - path: src/tube_multiscale_fusion/ablation_classifier.py
+      hash: md5
+      md5: d3feb508e850de3db730be7b10564d3a
+      size: 8872
+    - path: src/tube_multiscale_fusion/dataset.py
+      hash: md5
+      md5: d7d3e37c27fd25f7612751b3c5869dd2
+      size: 3036
+    - path: src/tube_multiscale_fusion/eval_plots.py
+      hash: md5
+      md5: f76a38b124a936129d9e0cc6910b9e33
+      size: 3399
+    - path: src/tube_multiscale_fusion/fusion.py
+      hash: md5
+      md5: 695dd2af05cb3a47cc3387c9c4ba210d
+      size: 7308
+    - path: src/tube_multiscale_fusion/lit_ablation.py
+      hash: md5
+      md5: 76a76ab01092ab2c2215724c9c0155e7
+      size: 13357
+    - path: src/tube_multiscale_fusion/local_branch.py
+      hash: md5
+      md5: 6fddb4c6db1fcb59c61261bed915b693
+      size: 19137
+    params:
+      params.yaml:
+        fusion:
+          d_fusion: 384
+          num_layers: 2
+          num_heads: 6
+          ffn_dim: 1536
+          dropout: 0.1
+        local_branch:
+          grid_size: 2
+          cell_size: 112
+          tube_length: 4
+          temporal_stride: 2
+          encoder:
+            t_kernel: 2
+            h_patch: 14
+            w_patch: 14
+            embed_dim: 384
+            num_layers: 2
+            num_heads: 6
+            ffn_dim: 1536
+            dropout: 0.1
+        train_spatial_tsm:
+          max_frames: 16
+          batch_size: 16
+          num_workers: 4
+          learning_rate: 0.0001
+          weight_decay: 0.05
+          max_epochs: 30
+          early_stop_patience: 5
+          seed: 42
+          head_hidden_dim: 256
+          head_dropout: 0.1
+          use_cosine_warmup: true
+          warmup_frac: 0.05
+          img_size: 224
+          ablation_variant: no_temporal
+          local_branch_section: local_branch
+          query_dim: 384
+          encoder_kind: tsm
+    outs:
+    - path: data/08_reporting/spatial_tsm/train
+      hash: md5
+      md5: b12e390c487af8c8777d7068b5353a21.dir
+      size: 587641
+      nfiles: 6
+  evaluate_spatial@resnet3d-train:
+    cmd: uv run python scripts/evaluate_ablation.py --data-dir 
+      data/05_model_input/train --checkpoint 
+      data/06_models/spatial_resnet3d/best_checkpoint.pt --output-dir 
+      data/08_reporting/spatial_resnet3d/train --params-path params.yaml 
+      --params-key train_spatial_resnet3d
+    deps:
+    - path: data/05_model_input/train
+      hash: md5
+      md5: 840f6c912a4c6ae3103627dd32594242.dir
+      size: 843667150
+      nfiles: 34039
+    - path: data/06_models/spatial_resnet3d
+      hash: md5
+      md5: dd66eec446fbc17992fc4e162546dcf2.dir
+      size: 487058223
+      nfiles: 5
+    - path: scripts/evaluate_ablation.py
+      hash: md5
+      md5: c3c9a3d0195471eb287ebd4a21b4321e
+      size: 5231
+    - path: src/tube_multiscale_fusion/ablation_classifier.py
+      hash: md5
+      md5: d3feb508e850de3db730be7b10564d3a
+      size: 8872
+    - path: src/tube_multiscale_fusion/dataset.py
+      hash: md5
+      md5: d7d3e37c27fd25f7612751b3c5869dd2
+      size: 3036
+    - path: src/tube_multiscale_fusion/eval_plots.py
+      hash: md5
+      md5: f76a38b124a936129d9e0cc6910b9e33
+      size: 3399
+    - path: src/tube_multiscale_fusion/fusion.py
+      hash: md5
+      md5: 695dd2af05cb3a47cc3387c9c4ba210d
+      size: 7308
+    - path: src/tube_multiscale_fusion/lit_ablation.py
+      hash: md5
+      md5: 76a76ab01092ab2c2215724c9c0155e7
+      size: 13357
+    - path: src/tube_multiscale_fusion/local_branch.py
+      hash: md5
+      md5: 6fddb4c6db1fcb59c61261bed915b693
+      size: 19137
+    params:
+      params.yaml:
+        fusion:
+          d_fusion: 384
+          num_layers: 2
+          num_heads: 6
+          ffn_dim: 1536
+          dropout: 0.1
+        local_branch:
+          grid_size: 2
+          cell_size: 112
+          tube_length: 4
+          temporal_stride: 2
+          encoder:
+            t_kernel: 2
+            h_patch: 14
+            w_patch: 14
+            embed_dim: 384
+            num_layers: 2
+            num_heads: 6
+            ffn_dim: 1536
+            dropout: 0.1
+        train_spatial_resnet3d:
+          max_frames: 16
+          batch_size: 8
+          num_workers: 4
+          learning_rate: 0.0001
+          weight_decay: 0.05
+          max_epochs: 30
+          early_stop_patience: 5
+          seed: 42
+          head_hidden_dim: 256
+          head_dropout: 0.1
+          use_cosine_warmup: true
+          warmup_frac: 0.05
+          img_size: 224
+          ablation_variant: no_temporal
+          local_branch_section: local_branch
+          query_dim: 384
+          encoder_kind: resnet3d
+    outs:
+    - path: data/08_reporting/spatial_resnet3d/train
+      hash: md5
+      md5: e1521609bc185e6393cde3a30e21f06e.dir
+      size: 582724
+      nfiles: 6
+  evaluate_spatial@vivit-val:
+    cmd: uv run python scripts/evaluate_ablation.py --data-dir 
+      data/05_model_input/val --checkpoint 
+      data/06_models/spatial_vivit/best_checkpoint.pt --output-dir 
+      data/08_reporting/spatial_vivit/val --params-path params.yaml --params-key
+      train_spatial_vivit
+    deps:
+    - path: data/05_model_input/val
+      hash: md5
+      md5: f8585e64f607a7a8d7d1651a9cbc930e.dir
+      size: 77725173
+      nfiles: 3219
+    - path: data/06_models/spatial_vivit
+      hash: md5
+      md5: 54028b5520975a36c70ad309d423317d.dir
+      size: 177496976
+      nfiles: 5
+    - path: scripts/evaluate_ablation.py
+      hash: md5
+      md5: c3c9a3d0195471eb287ebd4a21b4321e
+      size: 5231
+    - path: src/tube_multiscale_fusion/ablation_classifier.py
+      hash: md5
+      md5: d3feb508e850de3db730be7b10564d3a
+      size: 8872
+    - path: src/tube_multiscale_fusion/dataset.py
+      hash: md5
+      md5: d7d3e37c27fd25f7612751b3c5869dd2
+      size: 3036
+    - path: src/tube_multiscale_fusion/eval_plots.py
+      hash: md5
+      md5: f76a38b124a936129d9e0cc6910b9e33
+      size: 3399
+    - path: src/tube_multiscale_fusion/fusion.py
+      hash: md5
+      md5: 695dd2af05cb3a47cc3387c9c4ba210d
+      size: 7308
+    - path: src/tube_multiscale_fusion/lit_ablation.py
+      hash: md5
+      md5: 76a76ab01092ab2c2215724c9c0155e7
+      size: 13357
+    - path: src/tube_multiscale_fusion/local_branch.py
+      hash: md5
+      md5: 6fddb4c6db1fcb59c61261bed915b693
+      size: 19137
+    params:
+      params.yaml:
+        fusion:
+          d_fusion: 384
+          num_layers: 2
+          num_heads: 6
+          ffn_dim: 1536
+          dropout: 0.1
+        local_branch:
+          grid_size: 2
+          cell_size: 112
+          tube_length: 4
+          temporal_stride: 2
+          encoder:
+            t_kernel: 2
+            h_patch: 14
+            w_patch: 14
+            embed_dim: 384
+            num_layers: 2
+            num_heads: 6
+            ffn_dim: 1536
+            dropout: 0.1
+        train_spatial_vivit:
+          max_frames: 16
+          batch_size: 16
+          num_workers: 4
+          learning_rate: 0.0001
+          weight_decay: 0.05
+          max_epochs: 30
+          early_stop_patience: 5
+          seed: 42
+          head_hidden_dim: 256
+          head_dropout: 0.1
+          use_cosine_warmup: true
+          warmup_frac: 0.05
+          img_size: 224
+          ablation_variant: no_temporal
+          local_branch_section: local_branch
+          query_dim: 384
+          encoder_kind: vivit
+    outs:
+    - path: data/08_reporting/spatial_vivit/val
+      hash: md5
+      md5: 3fbed2834ebdea2548c95db5768ad94f.dir
+      size: 147439
+      nfiles: 6
+  train_full_combo@resnet3d_transformer:
+    cmd: uv run python scripts/train.py --train-dir data/05_model_input/train 
+      --val-dir data/05_model_input/val --output-dir 
+      data/06_models/full_resnet3d_transformer --params-path params.yaml 
+      --params-key train_full_resnet3d_transformer
+    deps:
+    - path: data/05_model_input/train
+      hash: md5
+      md5: 840f6c912a4c6ae3103627dd32594242.dir
+      size: 843667150
+      nfiles: 34039
+    - path: data/05_model_input/val
+      hash: md5
+      md5: f8585e64f607a7a8d7d1651a9cbc930e.dir
+      size: 77725173
+      nfiles: 3219
+    - path: scripts/train.py
+      hash: md5
+      md5: 3667951a8552a76e225529893e875118
+      size: 5579
+    - path: src/tube_multiscale_fusion/augment.py
+      hash: md5
+      md5: f52a42f92caa2fac859bf3a81a18971d
+      size: 9489
+    - path: src/tube_multiscale_fusion/classifier.py
+      hash: md5
+      md5: 84fd9cfc9373647c93c6aed124ea8544
+      size: 3799
+    - path: src/tube_multiscale_fusion/dataset.py
+      hash: md5
+      md5: d7d3e37c27fd25f7612751b3c5869dd2
+      size: 3036
+    - path: src/tube_multiscale_fusion/fusion.py
+      hash: md5
+      md5: 695dd2af05cb3a47cc3387c9c4ba210d
+      size: 7308
+    - path: src/tube_multiscale_fusion/global_branch.py
+      hash: md5
+      md5: 22bcbb284170752ff61a781a6119a474
+      size: 11333
+    - path: src/tube_multiscale_fusion/lit_module.py
+      hash: md5
+      md5: eb2bf5c272592ac2bc5236c8b356ad36
+      size: 7488
+    - path: src/tube_multiscale_fusion/local_branch.py
+      hash: md5
+      md5: 6fddb4c6db1fcb59c61261bed915b693
+      size: 19137
+    params:
+      params.yaml:
+        augment:
+          enabled: true
+          spatial:
+            flip_prob: 0.5
+            rotation_deg: 5.0
+            scale_range:
+            - 0.9
+            - 1.1
+            translate_frac: 0.05
+          photometric:
+            brightness_range:
+            - 0.8
+            - 1.2
+            contrast_range:
+            - 0.8
+            - 1.2
+            saturation_range:
+            - 0.8
+            - 1.2
+          temporal:
+            subseq_prob: 0.5
+            subseq_min_len: 4
+            stride_prob: 0.25
+            frame_drop_prob: 0.15
+            min_valid_after_drop: 4
+        fusion:
+          d_fusion: 384
+          num_layers: 2
+          num_heads: 6
+          ffn_dim: 1536
+          dropout: 0.1
+        global_branch:
+          backbone: vit_small_patch14_dinov2.lvd142m
+          aggregator:
+            num_layers: 2
+            num_heads: 6
+            ffn_dim: 1536
+            dropout: 0.1
+        local_branch:
+          grid_size: 2
+          cell_size: 112
+          tube_length: 4
+          temporal_stride: 2
+          encoder:
+            t_kernel: 2
+            h_patch: 14
+            w_patch: 14
+            embed_dim: 384
+            num_layers: 2
+            num_heads: 6
+            ffn_dim: 1536
+            dropout: 0.1
+        train_full_resnet3d_transformer:
+          max_frames: 16
+          batch_size: 8
+          num_workers: 4
+          learning_rate: 0.0001
+          weight_decay: 0.05
+          max_epochs: 30
+          early_stop_patience: 5
+          seed: 42
+          head_hidden_dim: 256
+          head_dropout: 0.1
+          use_cosine_warmup: true
+          warmup_frac: 0.05
+          img_size: 224
+          finetune: true
+          finetune_last_n_blocks: 1
+          backbone_lr: 1e-05
+          encoder_kind: resnet3d
+          aggregator_kind: transformer
+    outs:
+    - path: data/06_models/full_resnet3d_transformer
+      hash: md5
+      md5: b3b4d732446e73edca34ebe7947521d2.dir
+      size: 630529606
+      nfiles: 5
+  train_full_combo@vivit_lstm:
+    cmd: uv run python scripts/train.py --train-dir data/05_model_input/train 
+      --val-dir data/05_model_input/val --output-dir 
+      data/06_models/full_vivit_lstm --params-path params.yaml --params-key 
+      train_full_vivit_lstm
+    deps:
+    - path: data/05_model_input/train
+      hash: md5
+      md5: 840f6c912a4c6ae3103627dd32594242.dir
+      size: 843667150
+      nfiles: 34039
+    - path: data/05_model_input/val
+      hash: md5
+      md5: f8585e64f607a7a8d7d1651a9cbc930e.dir
+      size: 77725173
+      nfiles: 3219
+    - path: scripts/train.py
+      hash: md5
+      md5: 3667951a8552a76e225529893e875118
+      size: 5579
+    - path: src/tube_multiscale_fusion/augment.py
+      hash: md5
+      md5: f52a42f92caa2fac859bf3a81a18971d
+      size: 9489
+    - path: src/tube_multiscale_fusion/classifier.py
+      hash: md5
+      md5: 84fd9cfc9373647c93c6aed124ea8544
+      size: 3799
+    - path: src/tube_multiscale_fusion/dataset.py
+      hash: md5
+      md5: d7d3e37c27fd25f7612751b3c5869dd2
+      size: 3036
+    - path: src/tube_multiscale_fusion/fusion.py
+      hash: md5
+      md5: 695dd2af05cb3a47cc3387c9c4ba210d
+      size: 7308
+    - path: src/tube_multiscale_fusion/global_branch.py
+      hash: md5
+      md5: 22bcbb284170752ff61a781a6119a474
+      size: 11333
+    - path: src/tube_multiscale_fusion/lit_module.py
+      hash: md5
+      md5: eb2bf5c272592ac2bc5236c8b356ad36
+      size: 7488
+    - path: src/tube_multiscale_fusion/local_branch.py
+      hash: md5
+      md5: 6fddb4c6db1fcb59c61261bed915b693
+      size: 19137
+    params:
+      params.yaml:
+        augment:
+          enabled: true
+          spatial:
+            flip_prob: 0.5
+            rotation_deg: 5.0
+            scale_range:
+            - 0.9
+            - 1.1
+            translate_frac: 0.05
+          photometric:
+            brightness_range:
+            - 0.8
+            - 1.2
+            contrast_range:
+            - 0.8
+            - 1.2
+            saturation_range:
+            - 0.8
+            - 1.2
+          temporal:
+            subseq_prob: 0.5
+            subseq_min_len: 4
+            stride_prob: 0.25
+            frame_drop_prob: 0.15
+            min_valid_after_drop: 4
+        fusion:
+          d_fusion: 384
+          num_layers: 2
+          num_heads: 6
+          ffn_dim: 1536
+          dropout: 0.1
+        global_branch:
+          backbone: vit_small_patch14_dinov2.lvd142m
+          aggregator:
+            num_layers: 2
+            num_heads: 6
+            ffn_dim: 1536
+            dropout: 0.1
+        local_branch:
+          grid_size: 2
+          cell_size: 112
+          tube_length: 4
+          temporal_stride: 2
+          encoder:
+            t_kernel: 2
+            h_patch: 14
+            w_patch: 14
+            embed_dim: 384
+            num_layers: 2
+            num_heads: 6
+            ffn_dim: 1536
+            dropout: 0.1
+        train_full_vivit_lstm:
+          max_frames: 16
+          batch_size: 16
+          num_workers: 4
+          learning_rate: 0.0001
+          weight_decay: 0.05
+          max_epochs: 30
+          early_stop_patience: 5
+          seed: 42
+          head_hidden_dim: 256
+          head_dropout: 0.1
+          use_cosine_warmup: true
+          warmup_frac: 0.05
+          img_size: 224
+          finetune: true
+          finetune_last_n_blocks: 1
+          backbone_lr: 1e-05
+          encoder_kind: vivit
+          aggregator_kind: lstm
+    outs:
+    - path: data/06_models/full_vivit_lstm
+      hash: md5
+      md5: 60642e41db832623aff0d1c536015c94.dir
+      size: 292493642
+      nfiles: 5
+  evaluate_full_combo@vivit_lstm-val:
+    cmd: uv run python scripts/evaluate.py --data-dir data/05_model_input/val 
+      --checkpoint data/06_models/full_vivit_lstm/best_checkpoint.pt 
+      --output-dir data/08_reporting/full_vivit_lstm/val --params-path 
+      params.yaml --params-key train_full_vivit_lstm
+    deps:
+    - path: data/05_model_input/val
+      hash: md5
+      md5: f8585e64f607a7a8d7d1651a9cbc930e.dir
+      size: 77725173
+      nfiles: 3219
+    - path: data/06_models/full_vivit_lstm
+      hash: md5
+      md5: 60642e41db832623aff0d1c536015c94.dir
+      size: 292493642
+      nfiles: 5
+    - path: scripts/evaluate.py
+      hash: md5
+      md5: 44cba928007046cc57341989cd752879
+      size: 5231
+    - path: src/tube_multiscale_fusion/classifier.py
+      hash: md5
+      md5: 84fd9cfc9373647c93c6aed124ea8544
+      size: 3799
+    - path: src/tube_multiscale_fusion/dataset.py
+      hash: md5
+      md5: d7d3e37c27fd25f7612751b3c5869dd2
+      size: 3036
+    - path: src/tube_multiscale_fusion/eval_plots.py
+      hash: md5
+      md5: f76a38b124a936129d9e0cc6910b9e33
+      size: 3399
+    - path: src/tube_multiscale_fusion/fusion.py
+      hash: md5
+      md5: 695dd2af05cb3a47cc3387c9c4ba210d
+      size: 7308
+    - path: src/tube_multiscale_fusion/global_branch.py
+      hash: md5
+      md5: 22bcbb284170752ff61a781a6119a474
+      size: 11333
+    - path: src/tube_multiscale_fusion/lit_module.py
+      hash: md5
+      md5: eb2bf5c272592ac2bc5236c8b356ad36
+      size: 7488
+    - path: src/tube_multiscale_fusion/local_branch.py
+      hash: md5
+      md5: 6fddb4c6db1fcb59c61261bed915b693
+      size: 19137
+    params:
+      params.yaml:
+        fusion:
+          d_fusion: 384
+          num_layers: 2
+          num_heads: 6
+          ffn_dim: 1536
+          dropout: 0.1
+        global_branch:
+          backbone: vit_small_patch14_dinov2.lvd142m
+          aggregator:
+            num_layers: 2
+            num_heads: 6
+            ffn_dim: 1536
+            dropout: 0.1
+        local_branch:
+          grid_size: 2
+          cell_size: 112
+          tube_length: 4
+          temporal_stride: 2
+          encoder:
+            t_kernel: 2
+            h_patch: 14
+            w_patch: 14
+            embed_dim: 384
+            num_layers: 2
+            num_heads: 6
+            ffn_dim: 1536
+            dropout: 0.1
+        train_full_vivit_lstm:
+          max_frames: 16
+          batch_size: 16
+          num_workers: 4
+          learning_rate: 0.0001
+          weight_decay: 0.05
+          max_epochs: 30
+          early_stop_patience: 5
+          seed: 42
+          head_hidden_dim: 256
+          head_dropout: 0.1
+          use_cosine_warmup: true
+          warmup_frac: 0.05
+          img_size: 224
+          finetune: true
+          finetune_last_n_blocks: 1
+          backbone_lr: 1e-05
+          encoder_kind: vivit
+          aggregator_kind: lstm
+    outs:
+    - path: data/08_reporting/full_vivit_lstm/val
+      hash: md5
+      md5: 022530ad6392a37a45e3ef3f9e7e8062.dir
+      size: 140842
+      nfiles: 6
+  evaluate_full_combo@resnet3d_transformer-val:
+    cmd: uv run python scripts/evaluate.py --data-dir data/05_model_input/val 
+      --checkpoint data/06_models/full_resnet3d_transformer/best_checkpoint.pt 
+      --output-dir data/08_reporting/full_resnet3d_transformer/val --params-path
+      params.yaml --params-key train_full_resnet3d_transformer
+    deps:
+    - path: data/05_model_input/val
+      hash: md5
+      md5: f8585e64f607a7a8d7d1651a9cbc930e.dir
+      size: 77725173
+      nfiles: 3219
+    - path: data/06_models/full_resnet3d_transformer
+      hash: md5
+      md5: b3b4d732446e73edca34ebe7947521d2.dir
+      size: 630529606
+      nfiles: 5
+    - path: scripts/evaluate.py
+      hash: md5
+      md5: 44cba928007046cc57341989cd752879
+      size: 5231
+    - path: src/tube_multiscale_fusion/classifier.py
+      hash: md5
+      md5: 84fd9cfc9373647c93c6aed124ea8544
+      size: 3799
+    - path: src/tube_multiscale_fusion/dataset.py
+      hash: md5
+      md5: d7d3e37c27fd25f7612751b3c5869dd2
+      size: 3036
+    - path: src/tube_multiscale_fusion/eval_plots.py
+      hash: md5
+      md5: f76a38b124a936129d9e0cc6910b9e33
+      size: 3399
+    - path: src/tube_multiscale_fusion/fusion.py
+      hash: md5
+      md5: 695dd2af05cb3a47cc3387c9c4ba210d
+      size: 7308
+    - path: src/tube_multiscale_fusion/global_branch.py
+      hash: md5
+      md5: 22bcbb284170752ff61a781a6119a474
+      size: 11333
+    - path: src/tube_multiscale_fusion/lit_module.py
+      hash: md5
+      md5: eb2bf5c272592ac2bc5236c8b356ad36
+      size: 7488
+    - path: src/tube_multiscale_fusion/local_branch.py
+      hash: md5
+      md5: 6fddb4c6db1fcb59c61261bed915b693
+      size: 19137
+    params:
+      params.yaml:
+        fusion:
+          d_fusion: 384
+          num_layers: 2
+          num_heads: 6
+          ffn_dim: 1536
+          dropout: 0.1
+        global_branch:
+          backbone: vit_small_patch14_dinov2.lvd142m
+          aggregator:
+            num_layers: 2
+            num_heads: 6
+            ffn_dim: 1536
+            dropout: 0.1
+        local_branch:
+          grid_size: 2
+          cell_size: 112
+          tube_length: 4
+          temporal_stride: 2
+          encoder:
+            t_kernel: 2
+            h_patch: 14
+            w_patch: 14
+            embed_dim: 384
+            num_layers: 2
+            num_heads: 6
+            ffn_dim: 1536
+            dropout: 0.1
+        train_full_resnet3d_transformer:
+          max_frames: 16
+          batch_size: 8
+          num_workers: 4
+          learning_rate: 0.0001
+          weight_decay: 0.05
+          max_epochs: 30
+          early_stop_patience: 5
+          seed: 42
+          head_hidden_dim: 256
+          head_dropout: 0.1
+          use_cosine_warmup: true
+          warmup_frac: 0.05
+          img_size: 224
+          finetune: true
+          finetune_last_n_blocks: 1
+          backbone_lr: 1e-05
+          encoder_kind: resnet3d
+          aggregator_kind: transformer
+    outs:
+    - path: data/08_reporting/full_resnet3d_transformer/val
+      hash: md5
+      md5: e48d9db3fbd387e541febab0fc669a83.dir
+      size: 145075
+      nfiles: 6
+  train_full_combo@vivit_transformer:
+    cmd: uv run python scripts/train.py --train-dir data/05_model_input/train 
+      --val-dir data/05_model_input/val --output-dir 
+      data/06_models/full_vivit_transformer --params-path params.yaml 
+      --params-key train_full_vivit_transformer
+    deps:
+    - path: data/05_model_input/train
+      hash: md5
+      md5: 840f6c912a4c6ae3103627dd32594242.dir
+      size: 843667150
+      nfiles: 34039
+    - path: data/05_model_input/val
+      hash: md5
+      md5: f8585e64f607a7a8d7d1651a9cbc930e.dir
+      size: 77725173
+      nfiles: 3219
+    - path: scripts/train.py
+      hash: md5
+      md5: 3667951a8552a76e225529893e875118
+      size: 5579
+    - path: src/tube_multiscale_fusion/augment.py
+      hash: md5
+      md5: f52a42f92caa2fac859bf3a81a18971d
+      size: 9489
+    - path: src/tube_multiscale_fusion/classifier.py
+      hash: md5
+      md5: 84fd9cfc9373647c93c6aed124ea8544
+      size: 3799
+    - path: src/tube_multiscale_fusion/dataset.py
+      hash: md5
+      md5: d7d3e37c27fd25f7612751b3c5869dd2
+      size: 3036
+    - path: src/tube_multiscale_fusion/fusion.py
+      hash: md5
+      md5: 695dd2af05cb3a47cc3387c9c4ba210d
+      size: 7308
+    - path: src/tube_multiscale_fusion/global_branch.py
+      hash: md5
+      md5: 22bcbb284170752ff61a781a6119a474
+      size: 11333
+    - path: src/tube_multiscale_fusion/lit_module.py
+      hash: md5
+      md5: eb2bf5c272592ac2bc5236c8b356ad36
+      size: 7488
+    - path: src/tube_multiscale_fusion/local_branch.py
+      hash: md5
+      md5: 6fddb4c6db1fcb59c61261bed915b693
+      size: 19137
+    params:
+      params.yaml:
+        augment:
+          enabled: true
+          spatial:
+            flip_prob: 0.5
+            rotation_deg: 5.0
+            scale_range:
+            - 0.9
+            - 1.1
+            translate_frac: 0.05
+          photometric:
+            brightness_range:
+            - 0.8
+            - 1.2
+            contrast_range:
+            - 0.8
+            - 1.2
+            saturation_range:
+            - 0.8
+            - 1.2
+          temporal:
+            subseq_prob: 0.5
+            subseq_min_len: 4
+            stride_prob: 0.25
+            frame_drop_prob: 0.15
+            min_valid_after_drop: 4
+        fusion:
+          d_fusion: 384
+          num_layers: 2
+          num_heads: 6
+          ffn_dim: 1536
+          dropout: 0.1
+        global_branch:
+          backbone: vit_small_patch14_dinov2.lvd142m
+          aggregator:
+            num_layers: 2
+            num_heads: 6
+            ffn_dim: 1536
+            dropout: 0.1
+        local_branch:
+          grid_size: 2
+          cell_size: 112
+          tube_length: 4
+          temporal_stride: 2
+          encoder:
+            t_kernel: 2
+            h_patch: 14
+            w_patch: 14
+            embed_dim: 384
+            num_layers: 2
+            num_heads: 6
+            ffn_dim: 1536
+            dropout: 0.1
+        train_full_vivit_transformer:
+          max_frames: 16
+          batch_size: 16
+          num_workers: 4
+          learning_rate: 0.0001
+          weight_decay: 0.05
+          max_epochs: 30
+          early_stop_patience: 5
+          seed: 42
+          head_hidden_dim: 256
+          head_dropout: 0.1
+          use_cosine_warmup: true
+          warmup_frac: 0.05
+          img_size: 224
+          finetune: true
+          finetune_last_n_blocks: 1
+          backbone_lr: 1e-05
+          encoder_kind: vivit
+          aggregator_kind: transformer
+    outs:
+    - path: data/06_models/full_vivit_transformer
+      hash: md5
+      md5: 66e6f23985f3622554b2f80b6973f417.dir
+      size: 320986427
+      nfiles: 5
+  evaluate_full_combo@vivit_transformer-train:
+    cmd: uv run python scripts/evaluate.py --data-dir data/05_model_input/train 
+      --checkpoint data/06_models/full_vivit_transformer/best_checkpoint.pt 
+      --output-dir data/08_reporting/full_vivit_transformer/train --params-path 
+      params.yaml --params-key train_full_vivit_transformer
+    deps:
+    - path: data/05_model_input/train
+      hash: md5
+      md5: 840f6c912a4c6ae3103627dd32594242.dir
+      size: 843667150
+      nfiles: 34039
+    - path: data/06_models/full_vivit_transformer
+      hash: md5
+      md5: 66e6f23985f3622554b2f80b6973f417.dir
+      size: 320986427
+      nfiles: 5
+    - path: scripts/evaluate.py
+      hash: md5
+      md5: 44cba928007046cc57341989cd752879
+      size: 5231
+    - path: src/tube_multiscale_fusion/classifier.py
+      hash: md5
+      md5: 84fd9cfc9373647c93c6aed124ea8544
+      size: 3799
+    - path: src/tube_multiscale_fusion/dataset.py
+      hash: md5
+      md5: d7d3e37c27fd25f7612751b3c5869dd2
+      size: 3036
+    - path: src/tube_multiscale_fusion/eval_plots.py
+      hash: md5
+      md5: f76a38b124a936129d9e0cc6910b9e33
+      size: 3399
+    - path: src/tube_multiscale_fusion/fusion.py
+      hash: md5
+      md5: 695dd2af05cb3a47cc3387c9c4ba210d
+      size: 7308
+    - path: src/tube_multiscale_fusion/global_branch.py
+      hash: md5
+      md5: 22bcbb284170752ff61a781a6119a474
+      size: 11333
+    - path: src/tube_multiscale_fusion/lit_module.py
+      hash: md5
+      md5: eb2bf5c272592ac2bc5236c8b356ad36
+      size: 7488
+    - path: src/tube_multiscale_fusion/local_branch.py
+      hash: md5
+      md5: 6fddb4c6db1fcb59c61261bed915b693
+      size: 19137
+    params:
+      params.yaml:
+        fusion:
+          d_fusion: 384
+          num_layers: 2
+          num_heads: 6
+          ffn_dim: 1536
+          dropout: 0.1
+        global_branch:
+          backbone: vit_small_patch14_dinov2.lvd142m
+          aggregator:
+            num_layers: 2
+            num_heads: 6
+            ffn_dim: 1536
+            dropout: 0.1
+        local_branch:
+          grid_size: 2
+          cell_size: 112
+          tube_length: 4
+          temporal_stride: 2
+          encoder:
+            t_kernel: 2
+            h_patch: 14
+            w_patch: 14
+            embed_dim: 384
+            num_layers: 2
+            num_heads: 6
+            ffn_dim: 1536
+            dropout: 0.1
+        train_full_vivit_transformer:
+          max_frames: 16
+          batch_size: 16
+          num_workers: 4
+          learning_rate: 0.0001
+          weight_decay: 0.05
+          max_epochs: 30
+          early_stop_patience: 5
+          seed: 42
+          head_hidden_dim: 256
+          head_dropout: 0.1
+          use_cosine_warmup: true
+          warmup_frac: 0.05
+          img_size: 224
+          finetune: true
+          finetune_last_n_blocks: 1
+          backbone_lr: 1e-05
+          encoder_kind: vivit
+          aggregator_kind: transformer
+    outs:
+    - path: data/08_reporting/full_vivit_transformer/train
+      hash: md5
+      md5: 761548e996d7958f02e0e23dccf96299.dir
+      size: 581647
+      nfiles: 6
+  train_full_combo@resnet3d_lstm:
+    cmd: uv run python scripts/train.py --train-dir data/05_model_input/train 
+      --val-dir data/05_model_input/val --output-dir 
+      data/06_models/full_resnet3d_lstm --params-path params.yaml --params-key 
+      train_full_resnet3d_lstm
+    deps:
+    - path: data/05_model_input/train
+      hash: md5
+      md5: 840f6c912a4c6ae3103627dd32594242.dir
+      size: 843667150
+      nfiles: 34039
+    - path: data/05_model_input/val
+      hash: md5
+      md5: f8585e64f607a7a8d7d1651a9cbc930e.dir
+      size: 77725173
+      nfiles: 3219
+    - path: scripts/train.py
+      hash: md5
+      md5: 3667951a8552a76e225529893e875118
+      size: 5579
+    - path: src/tube_multiscale_fusion/augment.py
+      hash: md5
+      md5: f52a42f92caa2fac859bf3a81a18971d
+      size: 9489
+    - path: src/tube_multiscale_fusion/classifier.py
+      hash: md5
+      md5: 84fd9cfc9373647c93c6aed124ea8544
+      size: 3799
+    - path: src/tube_multiscale_fusion/dataset.py
+      hash: md5
+      md5: d7d3e37c27fd25f7612751b3c5869dd2
+      size: 3036
+    - path: src/tube_multiscale_fusion/fusion.py
+      hash: md5
+      md5: 695dd2af05cb3a47cc3387c9c4ba210d
+      size: 7308
+    - path: src/tube_multiscale_fusion/global_branch.py
+      hash: md5
+      md5: 22bcbb284170752ff61a781a6119a474
+      size: 11333
+    - path: src/tube_multiscale_fusion/lit_module.py
+      hash: md5
+      md5: eb2bf5c272592ac2bc5236c8b356ad36
+      size: 7488
+    - path: src/tube_multiscale_fusion/local_branch.py
+      hash: md5
+      md5: 6fddb4c6db1fcb59c61261bed915b693
+      size: 19137
+    params:
+      params.yaml:
+        augment:
+          enabled: true
+          spatial:
+            flip_prob: 0.5
+            rotation_deg: 5.0
+            scale_range:
+            - 0.9
+            - 1.1
+            translate_frac: 0.05
+          photometric:
+            brightness_range:
+            - 0.8
+            - 1.2
+            contrast_range:
+            - 0.8
+            - 1.2
+            saturation_range:
+            - 0.8
+            - 1.2
+          temporal:
+            subseq_prob: 0.5
+            subseq_min_len: 4
+            stride_prob: 0.25
+            frame_drop_prob: 0.15
+            min_valid_after_drop: 4
+        fusion:
+          d_fusion: 384
+          num_layers: 2
+          num_heads: 6
+          ffn_dim: 1536
+          dropout: 0.1
+        global_branch:
+          backbone: vit_small_patch14_dinov2.lvd142m
+          aggregator:
+            num_layers: 2
+            num_heads: 6
+            ffn_dim: 1536
+            dropout: 0.1
+        local_branch:
+          grid_size: 2
+          cell_size: 112
+          tube_length: 4
+          temporal_stride: 2
+          encoder:
+            t_kernel: 2
+            h_patch: 14
+            w_patch: 14
+            embed_dim: 384
+            num_layers: 2
+            num_heads: 6
+            ffn_dim: 1536
+            dropout: 0.1
+        train_full_resnet3d_lstm:
+          max_frames: 16
+          batch_size: 8
+          num_workers: 4
+          learning_rate: 0.0001
+          weight_decay: 0.05
+          max_epochs: 30
+          early_stop_patience: 5
+          seed: 42
+          head_hidden_dim: 256
+          head_dropout: 0.1
+          use_cosine_warmup: true
+          warmup_frac: 0.05
+          img_size: 224
+          finetune: true
+          finetune_last_n_blocks: 1
+          backbone_lr: 1e-05
+          encoder_kind: resnet3d
+          aggregator_kind: lstm
+    outs:
+    - path: data/06_models/full_resnet3d_lstm
+      hash: md5
+      md5: bd3c2c20b3ba98c4ee190a35f47348aa.dir
+      size: 602043193
+      nfiles: 5
+  evaluate_full_combo@resnet3d_transformer-train:
+    cmd: uv run python scripts/evaluate.py --data-dir data/05_model_input/train 
+      --checkpoint data/06_models/full_resnet3d_transformer/best_checkpoint.pt 
+      --output-dir data/08_reporting/full_resnet3d_transformer/train 
+      --params-path params.yaml --params-key train_full_resnet3d_transformer
+    deps:
+    - path: data/05_model_input/train
+      hash: md5
+      md5: 840f6c912a4c6ae3103627dd32594242.dir
+      size: 843667150
+      nfiles: 34039
+    - path: data/06_models/full_resnet3d_transformer
+      hash: md5
+      md5: b3b4d732446e73edca34ebe7947521d2.dir
+      size: 630529606
+      nfiles: 5
+    - path: scripts/evaluate.py
+      hash: md5
+      md5: 44cba928007046cc57341989cd752879
+      size: 5231
+    - path: src/tube_multiscale_fusion/classifier.py
+      hash: md5
+      md5: 84fd9cfc9373647c93c6aed124ea8544
+      size: 3799
+    - path: src/tube_multiscale_fusion/dataset.py
+      hash: md5
+      md5: d7d3e37c27fd25f7612751b3c5869dd2
+      size: 3036
+    - path: src/tube_multiscale_fusion/eval_plots.py
+      hash: md5
+      md5: f76a38b124a936129d9e0cc6910b9e33
+      size: 3399
+    - path: src/tube_multiscale_fusion/fusion.py
+      hash: md5
+      md5: 695dd2af05cb3a47cc3387c9c4ba210d
+      size: 7308
+    - path: src/tube_multiscale_fusion/global_branch.py
+      hash: md5
+      md5: 22bcbb284170752ff61a781a6119a474
+      size: 11333
+    - path: src/tube_multiscale_fusion/lit_module.py
+      hash: md5
+      md5: eb2bf5c272592ac2bc5236c8b356ad36
+      size: 7488
+    - path: src/tube_multiscale_fusion/local_branch.py
+      hash: md5
+      md5: 6fddb4c6db1fcb59c61261bed915b693
+      size: 19137
+    params:
+      params.yaml:
+        fusion:
+          d_fusion: 384
+          num_layers: 2
+          num_heads: 6
+          ffn_dim: 1536
+          dropout: 0.1
+        global_branch:
+          backbone: vit_small_patch14_dinov2.lvd142m
+          aggregator:
+            num_layers: 2
+            num_heads: 6
+            ffn_dim: 1536
+            dropout: 0.1
+        local_branch:
+          grid_size: 2
+          cell_size: 112
+          tube_length: 4
+          temporal_stride: 2
+          encoder:
+            t_kernel: 2
+            h_patch: 14
+            w_patch: 14
+            embed_dim: 384
+            num_layers: 2
+            num_heads: 6
+            ffn_dim: 1536
+            dropout: 0.1
+        train_full_resnet3d_transformer:
+          max_frames: 16
+          batch_size: 8
+          num_workers: 4
+          learning_rate: 0.0001
+          weight_decay: 0.05
+          max_epochs: 30
+          early_stop_patience: 5
+          seed: 42
+          head_hidden_dim: 256
+          head_dropout: 0.1
+          use_cosine_warmup: true
+          warmup_frac: 0.05
+          img_size: 224
+          finetune: true
+          finetune_last_n_blocks: 1
+          backbone_lr: 1e-05
+          encoder_kind: resnet3d
+          aggregator_kind: transformer
+    outs:
+    - path: data/08_reporting/full_resnet3d_transformer/train
+      hash: md5
+      md5: eafeee929e04ea55c6f062d63e01223c.dir
+      size: 583129
+      nfiles: 6
+  train_full_combo@tubelet_lstm:
+    cmd: uv run python scripts/train.py --train-dir data/05_model_input/train 
+      --val-dir data/05_model_input/val --output-dir 
+      data/06_models/full_tubelet_lstm --params-path params.yaml --params-key 
+      train_full_tubelet_lstm
+    deps:
+    - path: data/05_model_input/train
+      hash: md5
+      md5: 840f6c912a4c6ae3103627dd32594242.dir
+      size: 843667150
+      nfiles: 34039
+    - path: data/05_model_input/val
+      hash: md5
+      md5: f8585e64f607a7a8d7d1651a9cbc930e.dir
+      size: 77725173
+      nfiles: 3219
+    - path: scripts/train.py
+      hash: md5
+      md5: 3667951a8552a76e225529893e875118
+      size: 5579
+    - path: src/tube_multiscale_fusion/augment.py
+      hash: md5
+      md5: f52a42f92caa2fac859bf3a81a18971d
+      size: 9489
+    - path: src/tube_multiscale_fusion/classifier.py
+      hash: md5
+      md5: 84fd9cfc9373647c93c6aed124ea8544
+      size: 3799
+    - path: src/tube_multiscale_fusion/dataset.py
+      hash: md5
+      md5: d7d3e37c27fd25f7612751b3c5869dd2
+      size: 3036
+    - path: src/tube_multiscale_fusion/fusion.py
+      hash: md5
+      md5: 695dd2af05cb3a47cc3387c9c4ba210d
+      size: 7308
+    - path: src/tube_multiscale_fusion/global_branch.py
+      hash: md5
+      md5: 22bcbb284170752ff61a781a6119a474
+      size: 11333
+    - path: src/tube_multiscale_fusion/lit_module.py
+      hash: md5
+      md5: eb2bf5c272592ac2bc5236c8b356ad36
+      size: 7488
+    - path: src/tube_multiscale_fusion/local_branch.py
+      hash: md5
+      md5: 6fddb4c6db1fcb59c61261bed915b693
+      size: 19137
+    params:
+      params.yaml:
+        augment:
+          enabled: true
+          spatial:
+            flip_prob: 0.5
+            rotation_deg: 5.0
+            scale_range:
+            - 0.9
+            - 1.1
+            translate_frac: 0.05
+          photometric:
+            brightness_range:
+            - 0.8
+            - 1.2
+            contrast_range:
+            - 0.8
+            - 1.2
+            saturation_range:
+            - 0.8
+            - 1.2
+          temporal:
+            subseq_prob: 0.5
+            subseq_min_len: 4
+            stride_prob: 0.25
+            frame_drop_prob: 0.15
+            min_valid_after_drop: 4
+        fusion:
+          d_fusion: 384
+          num_layers: 2
+          num_heads: 6
+          ffn_dim: 1536
+          dropout: 0.1
+        global_branch:
+          backbone: vit_small_patch14_dinov2.lvd142m
+          aggregator:
+            num_layers: 2
+            num_heads: 6
+            ffn_dim: 1536
+            dropout: 0.1
+        local_branch:
+          grid_size: 2
+          cell_size: 112
+          tube_length: 4
+          temporal_stride: 2
+          encoder:
+            t_kernel: 2
+            h_patch: 14
+            w_patch: 14
+            embed_dim: 384
+            num_layers: 2
+            num_heads: 6
+            ffn_dim: 1536
+            dropout: 0.1
+        train_full_tubelet_lstm:
+          max_frames: 16
+          batch_size: 16
+          num_workers: 4
+          learning_rate: 0.0001
+          weight_decay: 0.05
+          max_epochs: 30
+          early_stop_patience: 5
+          seed: 42
+          head_hidden_dim: 256
+          head_dropout: 0.1
+          use_cosine_warmup: true
+          warmup_frac: 0.05
+          img_size: 224
+          finetune: true
+          finetune_last_n_blocks: 1
+          backbone_lr: 1e-05
+          encoder_kind: tubelet_transformer
+          aggregator_kind: lstm
+    outs:
+    - path: data/06_models/full_tubelet_lstm
+      hash: md5
+      md5: fb580c9abddb74b6c5230f159ec1505b.dir
+      size: 250181395
+      nfiles: 5
+  evaluate_full_combo@tubelet_lstm-train:
+    cmd: uv run python scripts/evaluate.py --data-dir data/05_model_input/train 
+      --checkpoint data/06_models/full_tubelet_lstm/best_checkpoint.pt 
+      --output-dir data/08_reporting/full_tubelet_lstm/train --params-path 
+      params.yaml --params-key train_full_tubelet_lstm
+    deps:
+    - path: data/05_model_input/train
+      hash: md5
+      md5: 840f6c912a4c6ae3103627dd32594242.dir
+      size: 843667150
+      nfiles: 34039
+    - path: data/06_models/full_tubelet_lstm
+      hash: md5
+      md5: fb580c9abddb74b6c5230f159ec1505b.dir
+      size: 250181395
+      nfiles: 5
+    - path: scripts/evaluate.py
+      hash: md5
+      md5: 44cba928007046cc57341989cd752879
+      size: 5231
+    - path: src/tube_multiscale_fusion/classifier.py
+      hash: md5
+      md5: 84fd9cfc9373647c93c6aed124ea8544
+      size: 3799
+    - path: src/tube_multiscale_fusion/dataset.py
+      hash: md5
+      md5: d7d3e37c27fd25f7612751b3c5869dd2
+      size: 3036
+    - path: src/tube_multiscale_fusion/eval_plots.py
+      hash: md5
+      md5: f76a38b124a936129d9e0cc6910b9e33
+      size: 3399
+    - path: src/tube_multiscale_fusion/fusion.py
+      hash: md5
+      md5: 695dd2af05cb3a47cc3387c9c4ba210d
+      size: 7308
+    - path: src/tube_multiscale_fusion/global_branch.py
+      hash: md5
+      md5: 22bcbb284170752ff61a781a6119a474
+      size: 11333
+    - path: src/tube_multiscale_fusion/lit_module.py
+      hash: md5
+      md5: eb2bf5c272592ac2bc5236c8b356ad36
+      size: 7488
+    - path: src/tube_multiscale_fusion/local_branch.py
+      hash: md5
+      md5: 6fddb4c6db1fcb59c61261bed915b693
+      size: 19137
+    params:
+      params.yaml:
+        fusion:
+          d_fusion: 384
+          num_layers: 2
+          num_heads: 6
+          ffn_dim: 1536
+          dropout: 0.1
+        global_branch:
+          backbone: vit_small_patch14_dinov2.lvd142m
+          aggregator:
+            num_layers: 2
+            num_heads: 6
+            ffn_dim: 1536
+            dropout: 0.1
+        local_branch:
+          grid_size: 2
+          cell_size: 112
+          tube_length: 4
+          temporal_stride: 2
+          encoder:
+            t_kernel: 2
+            h_patch: 14
+            w_patch: 14
+            embed_dim: 384
+            num_layers: 2
+            num_heads: 6
+            ffn_dim: 1536
+            dropout: 0.1
+        train_full_tubelet_lstm:
+          max_frames: 16
+          batch_size: 16
+          num_workers: 4
+          learning_rate: 0.0001
+          weight_decay: 0.05
+          max_epochs: 30
+          early_stop_patience: 5
+          seed: 42
+          head_hidden_dim: 256
+          head_dropout: 0.1
+          use_cosine_warmup: true
+          warmup_frac: 0.05
+          img_size: 224
+          finetune: true
+          finetune_last_n_blocks: 1
+          backbone_lr: 1e-05
+          encoder_kind: tubelet_transformer
+          aggregator_kind: lstm
+    outs:
+    - path: data/08_reporting/full_tubelet_lstm/train
+      hash: md5
+      md5: 18d0b799f9546abb89877e7253557d4a.dir
+      size: 579174
+      nfiles: 6
+  evaluate_full_combo@resnet3d_lstm-train:
+    cmd: uv run python scripts/evaluate.py --data-dir data/05_model_input/train 
+      --checkpoint data/06_models/full_resnet3d_lstm/best_checkpoint.pt 
+      --output-dir data/08_reporting/full_resnet3d_lstm/train --params-path 
+      params.yaml --params-key train_full_resnet3d_lstm
+    deps:
+    - path: data/05_model_input/train
+      hash: md5
+      md5: 840f6c912a4c6ae3103627dd32594242.dir
+      size: 843667150
+      nfiles: 34039
+    - path: data/06_models/full_resnet3d_lstm
+      hash: md5
+      md5: bd3c2c20b3ba98c4ee190a35f47348aa.dir
+      size: 602043193
+      nfiles: 5
+    - path: scripts/evaluate.py
+      hash: md5
+      md5: 44cba928007046cc57341989cd752879
+      size: 5231
+    - path: src/tube_multiscale_fusion/classifier.py
+      hash: md5
+      md5: 84fd9cfc9373647c93c6aed124ea8544
+      size: 3799
+    - path: src/tube_multiscale_fusion/dataset.py
+      hash: md5
+      md5: d7d3e37c27fd25f7612751b3c5869dd2
+      size: 3036
+    - path: src/tube_multiscale_fusion/eval_plots.py
+      hash: md5
+      md5: f76a38b124a936129d9e0cc6910b9e33
+      size: 3399
+    - path: src/tube_multiscale_fusion/fusion.py
+      hash: md5
+      md5: 695dd2af05cb3a47cc3387c9c4ba210d
+      size: 7308
+    - path: src/tube_multiscale_fusion/global_branch.py
+      hash: md5
+      md5: 22bcbb284170752ff61a781a6119a474
+      size: 11333
+    - path: src/tube_multiscale_fusion/lit_module.py
+      hash: md5
+      md5: eb2bf5c272592ac2bc5236c8b356ad36
+      size: 7488
+    - path: src/tube_multiscale_fusion/local_branch.py
+      hash: md5
+      md5: 6fddb4c6db1fcb59c61261bed915b693
+      size: 19137
+    params:
+      params.yaml:
+        fusion:
+          d_fusion: 384
+          num_layers: 2
+          num_heads: 6
+          ffn_dim: 1536
+          dropout: 0.1
+        global_branch:
+          backbone: vit_small_patch14_dinov2.lvd142m
+          aggregator:
+            num_layers: 2
+            num_heads: 6
+            ffn_dim: 1536
+            dropout: 0.1
+        local_branch:
+          grid_size: 2
+          cell_size: 112
+          tube_length: 4
+          temporal_stride: 2
+          encoder:
+            t_kernel: 2
+            h_patch: 14
+            w_patch: 14
+            embed_dim: 384
+            num_layers: 2
+            num_heads: 6
+            ffn_dim: 1536
+            dropout: 0.1
+        train_full_resnet3d_lstm:
+          max_frames: 16
+          batch_size: 8
+          num_workers: 4
+          learning_rate: 0.0001
+          weight_decay: 0.05
+          max_epochs: 30
+          early_stop_patience: 5
+          seed: 42
+          head_hidden_dim: 256
+          head_dropout: 0.1
+          use_cosine_warmup: true
+          warmup_frac: 0.05
+          img_size: 224
+          finetune: true
+          finetune_last_n_blocks: 1
+          backbone_lr: 1e-05
+          encoder_kind: resnet3d
+          aggregator_kind: lstm
+    outs:
+    - path: data/08_reporting/full_resnet3d_lstm/train
+      hash: md5
+      md5: 29b8ab208d72ea0da70472b804d90ae0.dir
+      size: 582690
+      nfiles: 6
+  evaluate_full_combo@vivit_lstm-train:
+    cmd: uv run python scripts/evaluate.py --data-dir data/05_model_input/train 
+      --checkpoint data/06_models/full_vivit_lstm/best_checkpoint.pt 
+      --output-dir data/08_reporting/full_vivit_lstm/train --params-path 
+      params.yaml --params-key train_full_vivit_lstm
+    deps:
+    - path: data/05_model_input/train
+      hash: md5
+      md5: 840f6c912a4c6ae3103627dd32594242.dir
+      size: 843667150
+      nfiles: 34039
+    - path: data/06_models/full_vivit_lstm
+      hash: md5
+      md5: 60642e41db832623aff0d1c536015c94.dir
+      size: 292493642
+      nfiles: 5
+    - path: scripts/evaluate.py
+      hash: md5
+      md5: 44cba928007046cc57341989cd752879
+      size: 5231
+    - path: src/tube_multiscale_fusion/classifier.py
+      hash: md5
+      md5: 84fd9cfc9373647c93c6aed124ea8544
+      size: 3799
+    - path: src/tube_multiscale_fusion/dataset.py
+      hash: md5
+      md5: d7d3e37c27fd25f7612751b3c5869dd2
+      size: 3036
+    - path: src/tube_multiscale_fusion/eval_plots.py
+      hash: md5
+      md5: f76a38b124a936129d9e0cc6910b9e33
+      size: 3399
+    - path: src/tube_multiscale_fusion/fusion.py
+      hash: md5
+      md5: 695dd2af05cb3a47cc3387c9c4ba210d
+      size: 7308
+    - path: src/tube_multiscale_fusion/global_branch.py
+      hash: md5
+      md5: 22bcbb284170752ff61a781a6119a474
+      size: 11333
+    - path: src/tube_multiscale_fusion/lit_module.py
+      hash: md5
+      md5: eb2bf5c272592ac2bc5236c8b356ad36
+      size: 7488
+    - path: src/tube_multiscale_fusion/local_branch.py
+      hash: md5
+      md5: 6fddb4c6db1fcb59c61261bed915b693
+      size: 19137
+    params:
+      params.yaml:
+        fusion:
+          d_fusion: 384
+          num_layers: 2
+          num_heads: 6
+          ffn_dim: 1536
+          dropout: 0.1
+        global_branch:
+          backbone: vit_small_patch14_dinov2.lvd142m
+          aggregator:
+            num_layers: 2
+            num_heads: 6
+            ffn_dim: 1536
+            dropout: 0.1
+        local_branch:
+          grid_size: 2
+          cell_size: 112
+          tube_length: 4
+          temporal_stride: 2
+          encoder:
+            t_kernel: 2
+            h_patch: 14
+            w_patch: 14
+            embed_dim: 384
+            num_layers: 2
+            num_heads: 6
+            ffn_dim: 1536
+            dropout: 0.1
+        train_full_vivit_lstm:
+          max_frames: 16
+          batch_size: 16
+          num_workers: 4
+          learning_rate: 0.0001
+          weight_decay: 0.05
+          max_epochs: 30
+          early_stop_patience: 5
+          seed: 42
+          head_hidden_dim: 256
+          head_dropout: 0.1
+          use_cosine_warmup: true
+          warmup_frac: 0.05
+          img_size: 224
+          finetune: true
+          finetune_last_n_blocks: 1
+          backbone_lr: 1e-05
+          encoder_kind: vivit
+          aggregator_kind: lstm
+    outs:
+    - path: data/08_reporting/full_vivit_lstm/train
+      hash: md5
+      md5: 08acf87dd2eb76d9e0aaac6bbf24d1e0.dir
+      size: 580171
+      nfiles: 6
+  evaluate_full_combo@tubelet_lstm-val:
+    cmd: uv run python scripts/evaluate.py --data-dir data/05_model_input/val 
+      --checkpoint data/06_models/full_tubelet_lstm/best_checkpoint.pt 
+      --output-dir data/08_reporting/full_tubelet_lstm/val --params-path 
+      params.yaml --params-key train_full_tubelet_lstm
+    deps:
+    - path: data/05_model_input/val
+      hash: md5
+      md5: f8585e64f607a7a8d7d1651a9cbc930e.dir
+      size: 77725173
+      nfiles: 3219
+    - path: data/06_models/full_tubelet_lstm
+      hash: md5
+      md5: fb580c9abddb74b6c5230f159ec1505b.dir
+      size: 250181395
+      nfiles: 5
+    - path: scripts/evaluate.py
+      hash: md5
+      md5: 44cba928007046cc57341989cd752879
+      size: 5231
+    - path: src/tube_multiscale_fusion/classifier.py
+      hash: md5
+      md5: 84fd9cfc9373647c93c6aed124ea8544
+      size: 3799
+    - path: src/tube_multiscale_fusion/dataset.py
+      hash: md5
+      md5: d7d3e37c27fd25f7612751b3c5869dd2
+      size: 3036
+    - path: src/tube_multiscale_fusion/eval_plots.py
+      hash: md5
+      md5: f76a38b124a936129d9e0cc6910b9e33
+      size: 3399
+    - path: src/tube_multiscale_fusion/fusion.py
+      hash: md5
+      md5: 695dd2af05cb3a47cc3387c9c4ba210d
+      size: 7308
+    - path: src/tube_multiscale_fusion/global_branch.py
+      hash: md5
+      md5: 22bcbb284170752ff61a781a6119a474
+      size: 11333
+    - path: src/tube_multiscale_fusion/lit_module.py
+      hash: md5
+      md5: eb2bf5c272592ac2bc5236c8b356ad36
+      size: 7488
+    - path: src/tube_multiscale_fusion/local_branch.py
+      hash: md5
+      md5: 6fddb4c6db1fcb59c61261bed915b693
+      size: 19137
+    params:
+      params.yaml:
+        fusion:
+          d_fusion: 384
+          num_layers: 2
+          num_heads: 6
+          ffn_dim: 1536
+          dropout: 0.1
+        global_branch:
+          backbone: vit_small_patch14_dinov2.lvd142m
+          aggregator:
+            num_layers: 2
+            num_heads: 6
+            ffn_dim: 1536
+            dropout: 0.1
+        local_branch:
+          grid_size: 2
+          cell_size: 112
+          tube_length: 4
+          temporal_stride: 2
+          encoder:
+            t_kernel: 2
+            h_patch: 14
+            w_patch: 14
+            embed_dim: 384
+            num_layers: 2
+            num_heads: 6
+            ffn_dim: 1536
+            dropout: 0.1
+        train_full_tubelet_lstm:
+          max_frames: 16
+          batch_size: 16
+          num_workers: 4
+          learning_rate: 0.0001
+          weight_decay: 0.05
+          max_epochs: 30
+          early_stop_patience: 5
+          seed: 42
+          head_hidden_dim: 256
+          head_dropout: 0.1
+          use_cosine_warmup: true
+          warmup_frac: 0.05
+          img_size: 224
+          finetune: true
+          finetune_last_n_blocks: 1
+          backbone_lr: 1e-05
+          encoder_kind: tubelet_transformer
+          aggregator_kind: lstm
+    outs:
+    - path: data/08_reporting/full_tubelet_lstm/val
+      hash: md5
+      md5: abf684fc89f0b53def2bc770b46d73e3.dir
+      size: 141389
+      nfiles: 6
+  evaluate_full_combo@vivit_transformer-val:
+    cmd: uv run python scripts/evaluate.py --data-dir data/05_model_input/val 
+      --checkpoint data/06_models/full_vivit_transformer/best_checkpoint.pt 
+      --output-dir data/08_reporting/full_vivit_transformer/val --params-path 
+      params.yaml --params-key train_full_vivit_transformer
+    deps:
+    - path: data/05_model_input/val
+      hash: md5
+      md5: f8585e64f607a7a8d7d1651a9cbc930e.dir
+      size: 77725173
+      nfiles: 3219
+    - path: data/06_models/full_vivit_transformer
+      hash: md5
+      md5: 66e6f23985f3622554b2f80b6973f417.dir
+      size: 320986427
+      nfiles: 5
+    - path: scripts/evaluate.py
+      hash: md5
+      md5: 44cba928007046cc57341989cd752879
+      size: 5231
+    - path: src/tube_multiscale_fusion/classifier.py
+      hash: md5
+      md5: 84fd9cfc9373647c93c6aed124ea8544
+      size: 3799
+    - path: src/tube_multiscale_fusion/dataset.py
+      hash: md5
+      md5: d7d3e37c27fd25f7612751b3c5869dd2
+      size: 3036
+    - path: src/tube_multiscale_fusion/eval_plots.py
+      hash: md5
+      md5: f76a38b124a936129d9e0cc6910b9e33
+      size: 3399
+    - path: src/tube_multiscale_fusion/fusion.py
+      hash: md5
+      md5: 695dd2af05cb3a47cc3387c9c4ba210d
+      size: 7308
+    - path: src/tube_multiscale_fusion/global_branch.py
+      hash: md5
+      md5: 22bcbb284170752ff61a781a6119a474
+      size: 11333
+    - path: src/tube_multiscale_fusion/lit_module.py
+      hash: md5
+      md5: eb2bf5c272592ac2bc5236c8b356ad36
+      size: 7488
+    - path: src/tube_multiscale_fusion/local_branch.py
+      hash: md5
+      md5: 6fddb4c6db1fcb59c61261bed915b693
+      size: 19137
+    params:
+      params.yaml:
+        fusion:
+          d_fusion: 384
+          num_layers: 2
+          num_heads: 6
+          ffn_dim: 1536
+          dropout: 0.1
+        global_branch:
+          backbone: vit_small_patch14_dinov2.lvd142m
+          aggregator:
+            num_layers: 2
+            num_heads: 6
+            ffn_dim: 1536
+            dropout: 0.1
+        local_branch:
+          grid_size: 2
+          cell_size: 112
+          tube_length: 4
+          temporal_stride: 2
+          encoder:
+            t_kernel: 2
+            h_patch: 14
+            w_patch: 14
+            embed_dim: 384
+            num_layers: 2
+            num_heads: 6
+            ffn_dim: 1536
+            dropout: 0.1
+        train_full_vivit_transformer:
+          max_frames: 16
+          batch_size: 16
+          num_workers: 4
+          learning_rate: 0.0001
+          weight_decay: 0.05
+          max_epochs: 30
+          early_stop_patience: 5
+          seed: 42
+          head_hidden_dim: 256
+          head_dropout: 0.1
+          use_cosine_warmup: true
+          warmup_frac: 0.05
+          img_size: 224
+          finetune: true
+          finetune_last_n_blocks: 1
+          backbone_lr: 1e-05
+          encoder_kind: vivit
+          aggregator_kind: transformer
+    outs:
+    - path: data/08_reporting/full_vivit_transformer/val
+      hash: md5
+      md5: 840500caf46fb58f9a65acba4cd19666.dir
+      size: 142970
+      nfiles: 6
+  evaluate_full_combo@resnet3d_lstm-val:
+    cmd: uv run python scripts/evaluate.py --data-dir data/05_model_input/val 
+      --checkpoint data/06_models/full_resnet3d_lstm/best_checkpoint.pt 
+      --output-dir data/08_reporting/full_resnet3d_lstm/val --params-path 
+      params.yaml --params-key train_full_resnet3d_lstm
+    deps:
+    - path: data/05_model_input/val
+      hash: md5
+      md5: f8585e64f607a7a8d7d1651a9cbc930e.dir
+      size: 77725173
+      nfiles: 3219
+    - path: data/06_models/full_resnet3d_lstm
+      hash: md5
+      md5: bd3c2c20b3ba98c4ee190a35f47348aa.dir
+      size: 602043193
+      nfiles: 5
+    - path: scripts/evaluate.py
+      hash: md5
+      md5: 44cba928007046cc57341989cd752879
+      size: 5231
+    - path: src/tube_multiscale_fusion/classifier.py
+      hash: md5
+      md5: 84fd9cfc9373647c93c6aed124ea8544
+      size: 3799
+    - path: src/tube_multiscale_fusion/dataset.py
+      hash: md5
+      md5: d7d3e37c27fd25f7612751b3c5869dd2
+      size: 3036
+    - path: src/tube_multiscale_fusion/eval_plots.py
+      hash: md5
+      md5: f76a38b124a936129d9e0cc6910b9e33
+      size: 3399
+    - path: src/tube_multiscale_fusion/fusion.py
+      hash: md5
+      md5: 695dd2af05cb3a47cc3387c9c4ba210d
+      size: 7308
+    - path: src/tube_multiscale_fusion/global_branch.py
+      hash: md5
+      md5: 22bcbb284170752ff61a781a6119a474
+      size: 11333
+    - path: src/tube_multiscale_fusion/lit_module.py
+      hash: md5
+      md5: eb2bf5c272592ac2bc5236c8b356ad36
+      size: 7488
+    - path: src/tube_multiscale_fusion/local_branch.py
+      hash: md5
+      md5: 6fddb4c6db1fcb59c61261bed915b693
+      size: 19137
+    params:
+      params.yaml:
+        fusion:
+          d_fusion: 384
+          num_layers: 2
+          num_heads: 6
+          ffn_dim: 1536
+          dropout: 0.1
+        global_branch:
+          backbone: vit_small_patch14_dinov2.lvd142m
+          aggregator:
+            num_layers: 2
+            num_heads: 6
+            ffn_dim: 1536
+            dropout: 0.1
+        local_branch:
+          grid_size: 2
+          cell_size: 112
+          tube_length: 4
+          temporal_stride: 2
+          encoder:
+            t_kernel: 2
+            h_patch: 14
+            w_patch: 14
+            embed_dim: 384
+            num_layers: 2
+            num_heads: 6
+            ffn_dim: 1536
+            dropout: 0.1
+        train_full_resnet3d_lstm:
+          max_frames: 16
+          batch_size: 8
+          num_workers: 4
+          learning_rate: 0.0001
+          weight_decay: 0.05
+          max_epochs: 30
+          early_stop_patience: 5
+          seed: 42
+          head_hidden_dim: 256
+          head_dropout: 0.1
+          use_cosine_warmup: true
+          warmup_frac: 0.05
+          img_size: 224
+          finetune: true
+          finetune_last_n_blocks: 1
+          backbone_lr: 1e-05
+          encoder_kind: resnet3d
+          aggregator_kind: lstm
+    outs:
+    - path: data/08_reporting/full_resnet3d_lstm/val
+      hash: md5
+      md5: d0727f5e83698e08ecfac2c7b195877d.dir
+      size: 143196
+      nfiles: 6
diff --git a/experiments/temporal-models/tube-multiscale-fusion/dvc.yaml b/experiments/temporal-models/tube-multiscale-fusion/dvc.yaml
new file mode 100644
index 00000000..7dfb5867
--- /dev/null
+++ b/experiments/temporal-models/tube-multiscale-fusion/dvc.yaml
@@ -0,0 +1,651 @@
+stages:
+  prepare:
+    desc: "Download YOLO detector weights from HuggingFace Hub (serving companion)"
+    cmd: >-
+      uv run python scripts/prepare.py
+      --model-repo ${prepare.model_repo}
+      --model-filename ${prepare.model_filename}
+      --output-model-dir data/01_raw/models
+    deps:
+      - scripts/prepare.py
+    params:
+      - prepare
+    outs:
+      - data/01_raw/models
+
+  truncate:
+    foreach:
+      - train
+      - val
+    do:
+      cmd: >-
+        uv run python scripts/truncate_sequences.py
+        --input-dir data/01_raw/datasets_full/${item}
+        --output-dir data/01_raw/datasets/${item}
+        --max-frames ${truncate.max_frames}
+      deps:
+        - scripts/truncate_sequences.py
+        - data/01_raw/datasets_full/${item}
+      params:
+        - truncate.max_frames
+      outs:
+        - data/01_raw/datasets/${item}
+
+  build_tubes:
+    foreach:
+      - train
+      - val
+    do:
+      cmd: >-
+        uv run python scripts/build_tubes.py
+        --input-dir data/01_raw/datasets/${item}
+        --output-dir data/03_primary/tubes/${item}
+        --iou-threshold ${tubes.iou_threshold}
+        --max-misses ${tubes.max_misses}
+        --min-tube-length ${build_tubes.min_tube_length}
+        --min-detected-entries ${build_tubes.min_detected_entries}
+      deps:
+        - scripts/build_tubes.py
+        - data/01_raw/datasets/${item}
+      params:
+        - tubes
+        - build_tubes
+      outs:
+        - data/03_primary/tubes/${item}
+
+  build_model_input:
+    foreach:
+      - train
+      - val
+    do:
+      cmd: >-
+        uv run python scripts/build_model_input.py
+        --tubes-dir data/03_primary/tubes/${item}
+        --raw-dir data/01_raw/datasets/${item}
+        --output-dir data/05_model_input/${item}
+        --context-factor ${model_input.context_factor}
+        --patch-size ${model_input.patch_size}
+      deps:
+        - scripts/build_model_input.py
+        - data/03_primary/tubes/${item}
+        - data/01_raw/datasets/${item}
+      params:
+        - model_input
+      outs:
+        - data/05_model_input/${item}
+
+  train_dinov2_multiscale:
+    cmd: >-
+      uv run python scripts/train.py
+      --train-dir data/05_model_input/train
+      --val-dir data/05_model_input/val
+      --output-dir data/06_models/dinov2_multiscale
+      --params-path params.yaml
+      --params-key train_dinov2_multiscale
+    deps:
+      - scripts/train.py
+      - src/tube_multiscale_fusion/augment.py
+      - src/tube_multiscale_fusion/dataset.py
+      - src/tube_multiscale_fusion/classifier.py
+      - src/tube_multiscale_fusion/global_branch.py
+      - src/tube_multiscale_fusion/local_branch.py
+      - src/tube_multiscale_fusion/fusion.py
+      - src/tube_multiscale_fusion/lit_module.py
+      - data/05_model_input/train
+      - data/05_model_input/val
+    params:
+      - train_dinov2_multiscale
+      - global_branch
+      - local_branch
+      - fusion
+      - augment
+    outs:
+      - data/06_models/dinov2_multiscale
+
+  # ---------------------------------------------------------------------------
+  # Ablation: full model minus the temporal module (global DINOv2 branch).
+  # Local branch + fusion are identical to the default model.
+  # ---------------------------------------------------------------------------
+  train_ablation_no_temporal:
+    cmd: >-
+      uv run python scripts/train_ablation.py
+      --train-dir data/05_model_input/train
+      --val-dir data/05_model_input/val
+      --output-dir data/06_models/ablation_no_temporal
+      --params-path params.yaml
+      --params-key train_ablation_no_temporal
+    deps:
+      - scripts/train_ablation.py
+      - src/tube_multiscale_fusion/augment.py
+      - src/tube_multiscale_fusion/dataset.py
+      - src/tube_multiscale_fusion/local_branch.py
+      - src/tube_multiscale_fusion/fusion.py
+      - src/tube_multiscale_fusion/ablation_classifier.py
+      - src/tube_multiscale_fusion/lit_ablation.py
+      - data/05_model_input/train
+      - data/05_model_input/val
+    params:
+      - train_ablation_no_temporal
+      - local_branch
+      - fusion
+      - augment
+    outs:
+      - data/06_models/ablation_no_temporal
+
+  evaluate_ablation_no_temporal:
+    foreach:
+      - train
+      - val
+    do:
+      cmd: >-
+        uv run python scripts/evaluate_ablation.py
+        --data-dir data/05_model_input/${item}
+        --checkpoint data/06_models/ablation_no_temporal/best_checkpoint.pt
+        --output-dir data/08_reporting/ablation_no_temporal/${item}
+        --params-path params.yaml
+        --params-key train_ablation_no_temporal
+      deps:
+        - scripts/evaluate_ablation.py
+        - src/tube_multiscale_fusion/dataset.py
+        - src/tube_multiscale_fusion/local_branch.py
+        - src/tube_multiscale_fusion/fusion.py
+        - src/tube_multiscale_fusion/ablation_classifier.py
+        - src/tube_multiscale_fusion/lit_ablation.py
+        - src/tube_multiscale_fusion/eval_plots.py
+        - data/05_model_input/${item}
+        - data/06_models/ablation_no_temporal
+      params:
+        - train_ablation_no_temporal
+        - local_branch
+        - fusion
+      outs:
+        - data/08_reporting/ablation_no_temporal/${item}
+
+  # ---------------------------------------------------------------------------
+  # Ablation: remove the spatial module (global branch only).
+  # ---------------------------------------------------------------------------
+  train_ablation_no_spatial:
+    cmd: >-
+      uv run python scripts/train_ablation_global.py
+      --train-dir data/05_model_input/train
+      --val-dir data/05_model_input/val
+      --output-dir data/06_models/ablation_no_spatial
+      --params-path params.yaml
+      --params-key train_ablation_no_spatial
+    deps:
+      - scripts/train_ablation_global.py
+      - src/tube_multiscale_fusion/augment.py
+      - src/tube_multiscale_fusion/dataset.py
+      - src/tube_multiscale_fusion/global_branch.py
+      - src/tube_multiscale_fusion/ablation_classifier.py
+      - src/tube_multiscale_fusion/lit_ablation.py
+      - data/05_model_input/train
+      - data/05_model_input/val
+    params:
+      - train_ablation_no_spatial
+      - global_branch
+      - augment
+    outs:
+      - data/06_models/ablation_no_spatial
+
+  evaluate_ablation_no_spatial:
+    foreach:
+      - train
+      - val
+    do:
+      cmd: >-
+        uv run python scripts/evaluate_ablation_global.py
+        --data-dir data/05_model_input/${item}
+        --checkpoint data/06_models/ablation_no_spatial/best_checkpoint.pt
+        --output-dir data/08_reporting/ablation_no_spatial/${item}
+        --params-path params.yaml
+        --params-key train_ablation_no_spatial
+      deps:
+        - scripts/evaluate_ablation_global.py
+        - src/tube_multiscale_fusion/dataset.py
+        - src/tube_multiscale_fusion/global_branch.py
+        - src/tube_multiscale_fusion/ablation_classifier.py
+        - src/tube_multiscale_fusion/lit_ablation.py
+        - src/tube_multiscale_fusion/eval_plots.py
+        - data/05_model_input/${item}
+        - data/06_models/ablation_no_spatial
+      params:
+        - train_ablation_no_spatial
+        - global_branch
+      outs:
+        - data/08_reporting/ablation_no_spatial/${item}
+
+  # ---------------------------------------------------------------------------
+  # Ablation: cross-attention fusion -> weighted means (both branches kept).
+  # ---------------------------------------------------------------------------
+  train_ablation_weighted_mean:
+    cmd: >-
+      uv run python scripts/train_ablation_global.py
+      --train-dir data/05_model_input/train
+      --val-dir data/05_model_input/val
+      --output-dir data/06_models/ablation_weighted_mean
+      --params-path params.yaml
+      --params-key train_ablation_weighted_mean
+    deps:
+      - scripts/train_ablation_global.py
+      - src/tube_multiscale_fusion/augment.py
+      - src/tube_multiscale_fusion/dataset.py
+      - src/tube_multiscale_fusion/global_branch.py
+      - src/tube_multiscale_fusion/local_branch.py
+      - src/tube_multiscale_fusion/fusion.py
+      - src/tube_multiscale_fusion/ablation_classifier.py
+      - src/tube_multiscale_fusion/lit_ablation.py
+      - data/05_model_input/train
+      - data/05_model_input/val
+    params:
+      - train_ablation_weighted_mean
+      - global_branch
+      - local_branch
+      - fusion
+      - augment
+    outs:
+      - data/06_models/ablation_weighted_mean
+
+  evaluate_ablation_weighted_mean:
+    foreach:
+      - train
+      - val
+    do:
+      cmd: >-
+        uv run python scripts/evaluate_ablation_global.py
+        --data-dir data/05_model_input/${item}
+        --checkpoint data/06_models/ablation_weighted_mean/best_checkpoint.pt
+        --output-dir data/08_reporting/ablation_weighted_mean/${item}
+        --params-path params.yaml
+        --params-key train_ablation_weighted_mean
+      deps:
+        - scripts/evaluate_ablation_global.py
+        - src/tube_multiscale_fusion/dataset.py
+        - src/tube_multiscale_fusion/global_branch.py
+        - src/tube_multiscale_fusion/local_branch.py
+        - src/tube_multiscale_fusion/fusion.py
+        - src/tube_multiscale_fusion/ablation_classifier.py
+        - src/tube_multiscale_fusion/lit_ablation.py
+        - src/tube_multiscale_fusion/eval_plots.py
+        - data/05_model_input/${item}
+        - data/06_models/ablation_weighted_mean
+      params:
+        - train_ablation_weighted_mean
+        - global_branch
+        - local_branch
+        - fusion
+      outs:
+        - data/08_reporting/ablation_weighted_mean/${item}
+
+  # ---------------------------------------------------------------------------
+  # Temporal-module comparison — global-only, varying the aggregator.
+  # (Transformer baseline = train/evaluate_ablation_no_spatial above.)
+  # ---------------------------------------------------------------------------
+  train_temporal:
+    matrix:
+      agg:
+        - lstm
+        - gru
+        - mlp
+        - conv1d
+        - linear_wavg
+    cmd: >-
+      uv run python scripts/train_ablation_global.py
+      --train-dir data/05_model_input/train
+      --val-dir data/05_model_input/val
+      --output-dir data/06_models/temporal_${item.agg}
+      --params-path params.yaml
+      --params-key train_temporal_${item.agg}
+    deps:
+      - scripts/train_ablation_global.py
+      - src/tube_multiscale_fusion/augment.py
+      - src/tube_multiscale_fusion/dataset.py
+      - src/tube_multiscale_fusion/global_branch.py
+      - src/tube_multiscale_fusion/ablation_classifier.py
+      - src/tube_multiscale_fusion/lit_ablation.py
+      - data/05_model_input/train
+      - data/05_model_input/val
+    params:
+      - train_temporal_${item.agg}
+      - global_branch
+      - augment
+    outs:
+      - data/06_models/temporal_${item.agg}
+
+  evaluate_temporal:
+    matrix:
+      agg:
+        - lstm
+        - gru
+        - mlp
+        - conv1d
+        - linear_wavg
+      split:
+        - train
+        - val
+    cmd: >-
+      uv run python scripts/evaluate_ablation_global.py
+      --data-dir data/05_model_input/${item.split}
+      --checkpoint data/06_models/temporal_${item.agg}/best_checkpoint.pt
+      --output-dir data/08_reporting/temporal_${item.agg}/${item.split}
+      --params-path params.yaml
+      --params-key train_temporal_${item.agg}
+    deps:
+      - scripts/evaluate_ablation_global.py
+      - src/tube_multiscale_fusion/dataset.py
+      - src/tube_multiscale_fusion/global_branch.py
+      - src/tube_multiscale_fusion/ablation_classifier.py
+      - src/tube_multiscale_fusion/lit_ablation.py
+      - src/tube_multiscale_fusion/eval_plots.py
+      - data/05_model_input/${item.split}
+      - data/06_models/temporal_${item.agg}
+    params:
+      - train_temporal_${item.agg}
+      - global_branch
+    outs:
+      - data/08_reporting/temporal_${item.agg}/${item.split}
+
+  # ---------------------------------------------------------------------------
+  # Spatial-module comparison — local-only, varying the per-tube encoder.
+  # (Tubelet-transformer baseline = train/evaluate_ablation_no_temporal above.)
+  # ---------------------------------------------------------------------------
+  train_spatial:
+    matrix:
+      enc:
+        - resnet3d
+        - vivit
+        - convlstm
+        - tsm
+    cmd: >-
+      uv run python scripts/train_ablation.py
+      --train-dir data/05_model_input/train
+      --val-dir data/05_model_input/val
+      --output-dir data/06_models/spatial_${item.enc}
+      --params-path params.yaml
+      --params-key train_spatial_${item.enc}
+    deps:
+      - scripts/train_ablation.py
+      - src/tube_multiscale_fusion/augment.py
+      - src/tube_multiscale_fusion/dataset.py
+      - src/tube_multiscale_fusion/local_branch.py
+      - src/tube_multiscale_fusion/fusion.py
+      - src/tube_multiscale_fusion/ablation_classifier.py
+      - src/tube_multiscale_fusion/lit_ablation.py
+      - data/05_model_input/train
+      - data/05_model_input/val
+    params:
+      - train_spatial_${item.enc}
+      - local_branch
+      - fusion
+      - augment
+    outs:
+      - data/06_models/spatial_${item.enc}
+
+  evaluate_spatial:
+    matrix:
+      enc:
+        - resnet3d
+        - vivit
+        - convlstm
+        - tsm
+      split:
+        - train
+        - val
+    cmd: >-
+      uv run python scripts/evaluate_ablation.py
+      --data-dir data/05_model_input/${item.split}
+      --checkpoint data/06_models/spatial_${item.enc}/best_checkpoint.pt
+      --output-dir data/08_reporting/spatial_${item.enc}/${item.split}
+      --params-path params.yaml
+      --params-key train_spatial_${item.enc}
+    deps:
+      - scripts/evaluate_ablation.py
+      - src/tube_multiscale_fusion/dataset.py
+      - src/tube_multiscale_fusion/local_branch.py
+      - src/tube_multiscale_fusion/fusion.py
+      - src/tube_multiscale_fusion/ablation_classifier.py
+      - src/tube_multiscale_fusion/lit_ablation.py
+      - src/tube_multiscale_fusion/eval_plots.py
+      - data/05_model_input/${item.split}
+      - data/06_models/spatial_${item.enc}
+    params:
+      - train_spatial_${item.enc}
+      - local_branch
+      - fusion
+    outs:
+      - data/08_reporting/spatial_${item.enc}/${item.split}
+
+  # ---------------------------------------------------------------------------
+  # Combined variations — full model, spatial x temporal cartesian product.
+  # (tubelet x transformer = train/evaluate_dinov2_multiscale above.)
+  # ---------------------------------------------------------------------------
+  train_full_combo:
+    matrix:
+      combo:
+        - resnet3d_lstm
+        - resnet3d_transformer
+        - vivit_lstm
+        - vivit_transformer
+        - tubelet_lstm
+    cmd: >-
+      uv run python scripts/train.py
+      --train-dir data/05_model_input/train
+      --val-dir data/05_model_input/val
+      --output-dir data/06_models/full_${item.combo}
+      --params-path params.yaml
+      --params-key train_full_${item.combo}
+    deps:
+      - scripts/train.py
+      - src/tube_multiscale_fusion/augment.py
+      - src/tube_multiscale_fusion/dataset.py
+      - src/tube_multiscale_fusion/classifier.py
+      - src/tube_multiscale_fusion/global_branch.py
+      - src/tube_multiscale_fusion/local_branch.py
+      - src/tube_multiscale_fusion/fusion.py
+      - src/tube_multiscale_fusion/lit_module.py
+      - data/05_model_input/train
+      - data/05_model_input/val
+    params:
+      - train_full_${item.combo}
+      - global_branch
+      - local_branch
+      - fusion
+      - augment
+    outs:
+      - data/06_models/full_${item.combo}
+
+  evaluate_full_combo:
+    matrix:
+      combo:
+        - resnet3d_lstm
+        - resnet3d_transformer
+        - vivit_lstm
+        - vivit_transformer
+        - tubelet_lstm
+      split:
+        - train
+        - val
+    cmd: >-
+      uv run python scripts/evaluate.py
+      --data-dir data/05_model_input/${item.split}
+      --checkpoint data/06_models/full_${item.combo}/best_checkpoint.pt
+      --output-dir data/08_reporting/full_${item.combo}/${item.split}
+      --params-path params.yaml
+      --params-key train_full_${item.combo}
+    deps:
+      - scripts/evaluate.py
+      - src/tube_multiscale_fusion/dataset.py
+      - src/tube_multiscale_fusion/classifier.py
+      - src/tube_multiscale_fusion/global_branch.py
+      - src/tube_multiscale_fusion/local_branch.py
+      - src/tube_multiscale_fusion/fusion.py
+      - src/tube_multiscale_fusion/lit_module.py
+      - src/tube_multiscale_fusion/eval_plots.py
+      - data/05_model_input/${item.split}
+      - data/06_models/full_${item.combo}
+    params:
+      - train_full_${item.combo}
+      - global_branch
+      - local_branch
+      - fusion
+    outs:
+      - data/08_reporting/full_${item.combo}/${item.split}
+
+  benchmark_dinov2_multiscale:
+    cmd: >-
+      uv run python scripts/benchmark.py
+      --data-dir data/05_model_input/val
+      --checkpoint data/06_models/dinov2_multiscale/best_checkpoint.pt
+      --predictions data/08_reporting/dinov2_multiscale/val/predictions.json
+      --output data/08_reporting/dinov2_multiscale_benchmark/benchmark.json
+    deps:
+      - scripts/benchmark.py
+      - src/tube_multiscale_fusion/dataset.py
+      - src/tube_multiscale_fusion/classifier.py
+      - src/tube_multiscale_fusion/global_branch.py
+      - src/tube_multiscale_fusion/local_branch.py
+      - src/tube_multiscale_fusion/fusion.py
+      - src/tube_multiscale_fusion/lit_module.py
+      - data/05_model_input/val
+      - data/06_models/dinov2_multiscale
+      - data/08_reporting/dinov2_multiscale/val
+    params:
+      - train_dinov2_multiscale
+      - global_branch
+      - local_branch
+      - fusion
+    metrics:
+      - data/08_reporting/dinov2_multiscale_benchmark/benchmark.json:
+          cache: false
+
+  evaluate_dinov2_multiscale:
+    foreach:
+      - train
+      - val
+    do:
+      cmd: >-
+        uv run python scripts/evaluate.py
+        --data-dir data/05_model_input/${item}
+        --checkpoint data/06_models/dinov2_multiscale/best_checkpoint.pt
+        --output-dir data/08_reporting/dinov2_multiscale/${item}
+        --params-path params.yaml
+        --params-key train_dinov2_multiscale
+      deps:
+        - scripts/evaluate.py
+        - src/tube_multiscale_fusion/dataset.py
+        - src/tube_multiscale_fusion/classifier.py
+        - src/tube_multiscale_fusion/global_branch.py
+        - src/tube_multiscale_fusion/local_branch.py
+        - src/tube_multiscale_fusion/fusion.py
+        - src/tube_multiscale_fusion/lit_module.py
+        - src/tube_multiscale_fusion/eval_plots.py
+        - data/05_model_input/${item}
+        - data/06_models/dinov2_multiscale
+      params:
+        - train_dinov2_multiscale
+        - global_branch
+        - local_branch
+        - fusion
+      outs:
+        - data/08_reporting/dinov2_multiscale/${item}
+
+  # ---------------------------------------------------------------------------
+  # Resolution sweep — same architecture, different local_branch geometry.
+  # ---------------------------------------------------------------------------
+  train_dinov2_multiscale_sweep:
+    matrix:
+      variant:
+        - spatial_4x4
+        - spatial_8x8
+        - temporal_stride1
+        - temporal_len8
+    cmd: >-
+      uv run python scripts/train.py
+      --train-dir data/05_model_input/train
+      --val-dir data/05_model_input/val
+      --output-dir data/06_models/dinov2_multiscale_${item.variant}
+      --params-path params.yaml
+      --params-key train_dinov2_multiscale_${item.variant}
+    deps:
+      - scripts/train.py
+      - src/tube_multiscale_fusion/augment.py
+      - src/tube_multiscale_fusion/dataset.py
+      - src/tube_multiscale_fusion/classifier.py
+      - src/tube_multiscale_fusion/global_branch.py
+      - src/tube_multiscale_fusion/local_branch.py
+      - src/tube_multiscale_fusion/fusion.py
+      - src/tube_multiscale_fusion/lit_module.py
+      - data/05_model_input/train
+      - data/05_model_input/val
+    params:
+      - train_dinov2_multiscale_${item.variant}
+      - global_branch
+      - local_branch_${item.variant}
+      - fusion
+      - augment
+    outs:
+      - data/06_models/dinov2_multiscale_${item.variant}
+
+  package_dinov2_multiscale:
+    cmd: >-
+      uv run python scripts/package_model.py
+      --checkpoint data/06_models/dinov2_multiscale/best_checkpoint.pt
+      --params-path params.yaml
+      --params-key train_dinov2_multiscale
+      --yolo-weights-path data/01_raw/models/best.pt
+      --val-patches-dir data/05_model_input/val
+      --output data/07_model_output/dinov2_multiscale/model_package.zip
+    deps:
+      - scripts/package_model.py
+      - src/tube_multiscale_fusion/package.py
+      - src/tube_multiscale_fusion/model.py
+      - data/06_models/dinov2_multiscale
+      - data/01_raw/models/best.pt
+      - data/05_model_input/val
+    params:
+      - package
+      - tubes
+      - build_tubes
+      - model_input
+      - train_dinov2_multiscale
+      - global_branch
+      - local_branch
+      - fusion
+    outs:
+      - data/07_model_output/dinov2_multiscale/model_package.zip
+
+  evaluate_dinov2_multiscale_sweep:
+    matrix:
+      variant:
+        - spatial_4x4
+        - spatial_8x8
+        - temporal_stride1
+        - temporal_len8
+      split:
+        - train
+        - val
+    cmd: >-
+      uv run python scripts/evaluate.py
+      --data-dir data/05_model_input/${item.split}
+      --checkpoint data/06_models/dinov2_multiscale_${item.variant}/best_checkpoint.pt
+      --output-dir data/08_reporting/dinov2_multiscale_${item.variant}/${item.split}
+      --params-path params.yaml
+      --params-key train_dinov2_multiscale_${item.variant}
+    deps:
+      - scripts/evaluate.py
+      - src/tube_multiscale_fusion/dataset.py
+      - src/tube_multiscale_fusion/classifier.py
+      - src/tube_multiscale_fusion/global_branch.py
+      - src/tube_multiscale_fusion/local_branch.py
+      - src/tube_multiscale_fusion/fusion.py
+      - src/tube_multiscale_fusion/lit_module.py
+      - src/tube_multiscale_fusion/eval_plots.py
+      - data/05_model_input/${item.split}
+      - data/06_models/dinov2_multiscale_${item.variant}
+    params:
+      - train_dinov2_multiscale_${item.variant}
+      - global_branch
+      - local_branch_${item.variant}
+      - fusion
+    outs:
+      - data/08_reporting/dinov2_multiscale_${item.variant}/${item.split}
diff --git a/experiments/temporal-models/tube-multiscale-fusion/notebooks/.gitkeep b/experiments/temporal-models/tube-multiscale-fusion/notebooks/.gitkeep
new file mode 100644
index 00000000..e69de29b
diff --git a/experiments/temporal-models/tube-multiscale-fusion/params.yaml b/experiments/temporal-models/tube-multiscale-fusion/params.yaml
new file mode 100644
index 00000000..2ddbd9af
--- /dev/null
+++ b/experiments/temporal-models/tube-multiscale-fusion/params.yaml
@@ -0,0 +1,356 @@
+# Keys prefixed with `_` are shared defaults consumed via YAML merge keys
+# (`<<: *anchor`). They are NOT DVC stage parameters.
+
+prepare:
+  model_repo: "pyronear/yolo11s_nimble-narwhal_v6.0.0"
+  model_filename: "best.pt"
+
+truncate:
+  max_frames: 16              # the global branch resolution
+
+tubes:
+  iou_threshold: 0.2
+  max_misses: 2
+
+build_tubes:
+  min_tube_length: 4
+  min_detected_entries: 2
+
+model_input:
+  context_factor: 1.5
+  patch_size: 224
+
+# ---------------------------------------------------------------------------
+# Serving / packaging — used by scripts/package_model.py to build the
+# leaderboard-comparable model archive (YOLO companion + classifier + config).
+# ---------------------------------------------------------------------------
+package:
+  target_recall: 0.95          # decision threshold calibrated to this recall on val
+  infer_min_tube_length: 2     # smallest tube prefix scored at inference
+  infer:
+    confidence_threshold: 0.1
+    iou_nms: 0.2
+    image_size: 1024
+    pad_to_min_frames: 16      # match truncate.max_frames / the global branch
+    pad_strategy: symmetric
+
+# ---------------------------------------------------------------------------
+# Global branch: DINOv2 per-frame + sequence transformer aggregator.
+# ---------------------------------------------------------------------------
+global_branch:
+  backbone: vit_small_patch14_dinov2.lvd142m   # feat_dim = 384
+  aggregator:
+    num_layers: 2
+    num_heads: 6
+    ffn_dim: 1536
+    dropout: 0.1
+
+# ---------------------------------------------------------------------------
+# Local branch: spatio-temporal tube extraction + video transformer encoder.
+#
+# Default geometry (winner of the resolution sweep): coarse 2x2 grid of
+# 112x112 cells x overlapping 4-frame windows -> 7 windows * 4 cells = 28
+# tubes per sequence. See README.md for sweep table.
+# ---------------------------------------------------------------------------
+local_branch:
+  grid_size: 2                # 2x2 spatial grid -> cell_size = patch_size / grid_size = 112
+  cell_size: 112
+  tube_length: 4
+  temporal_stride: 2          # 50% overlap (set to tube_length for no overlap)
+  encoder:
+    t_kernel: 2               # tubelet conv kernel along time
+    h_patch: 14               # tubelet conv kernel along height
+    w_patch: 14               # tubelet conv kernel along width
+    embed_dim: 384            # matches DINOv2 ViT-S feat_dim
+    num_layers: 2
+    num_heads: 6
+    ffn_dim: 1536
+    dropout: 0.1
+
+# ---------------------------------------------------------------------------
+# Fusion: self-attn over locals + cross-attn (global=Q, locals=KV).
+# ---------------------------------------------------------------------------
+fusion:
+  d_fusion: 384
+  num_layers: 2
+  num_heads: 6
+  ffn_dim: 1536
+  dropout: 0.1
+
+# ---------------------------------------------------------------------------
+# Training variants
+# ---------------------------------------------------------------------------
+_train_defaults: &train_defaults
+  max_frames: 16
+  batch_size: 16
+  num_workers: 4
+  learning_rate: 0.0001
+  weight_decay: 0.05
+  max_epochs: 30
+  early_stop_patience: 5
+  seed: 42
+  head_hidden_dim: 256
+  head_dropout: 0.1
+  use_cosine_warmup: true
+  warmup_frac: 0.05
+  img_size: 224
+
+train_dinov2_multiscale:
+  <<: *train_defaults
+  finetune: true
+  finetune_last_n_blocks: 1
+  backbone_lr: 0.00001
+
+# ---------------------------------------------------------------------------
+# Combined variations — full two-branch model, cartesian product of
+# spatial (local encoder) x temporal (global aggregator). See
+# COMBINED_VARIATIONS.md. The tubelet_transformer x transformer cell is the
+# production default (= train_dinov2_multiscale above), reused there.
+# ---------------------------------------------------------------------------
+_full_combo_defaults: &full_combo_defaults
+  <<: *train_defaults
+  finetune: true
+  finetune_last_n_blocks: 1
+  backbone_lr: 0.00001
+
+train_full_resnet3d_lstm:
+  <<: *full_combo_defaults
+  encoder_kind: resnet3d
+  aggregator_kind: lstm
+  batch_size: 8            # r3d_18 local encoder over 28 tubes is memory-heavy
+
+train_full_resnet3d_transformer:
+  <<: *full_combo_defaults
+  encoder_kind: resnet3d
+  aggregator_kind: transformer
+  batch_size: 8
+
+train_full_vivit_lstm:
+  <<: *full_combo_defaults
+  encoder_kind: vivit
+  aggregator_kind: lstm
+
+train_full_vivit_transformer:
+  <<: *full_combo_defaults
+  encoder_kind: vivit
+  aggregator_kind: transformer
+
+train_full_tubelet_lstm:
+  <<: *full_combo_defaults
+  encoder_kind: tubelet_transformer
+  aggregator_kind: lstm
+
+# ---------------------------------------------------------------------------
+# Resolution sweep — alternative local_branch geometries kept as reference.
+#
+# Each block changes spatial grid and/or temporal slicing; the encoder
+# tubelet kernel is sized to the cell so tubelet conv always emits at least
+# one spatial token. Total tube count = n_temporal_windows * grid_size**2.
+# ---------------------------------------------------------------------------
+
+# Baseline 4x4 grid of 56x56 cells -> 112 tubes/seq (previous default).
+local_branch_spatial_4x4:
+  grid_size: 4
+  cell_size: 56
+  tube_length: 4
+  temporal_stride: 2
+  encoder:
+    t_kernel: 2
+    h_patch: 14
+    w_patch: 14
+    embed_dim: 384
+    num_layers: 2
+    num_heads: 6
+    ffn_dim: 1536
+    dropout: 0.1
+
+# Fine spatial (8x8 grid of 28x28 cells -> 448 tubes/seq with default time).
+local_branch_spatial_8x8:
+  grid_size: 8
+  cell_size: 28
+  tube_length: 4
+  temporal_stride: 2
+  encoder:
+    t_kernel: 2
+    h_patch: 14
+    w_patch: 14
+    embed_dim: 384
+    num_layers: 2
+    num_heads: 6
+    ffn_dim: 1536
+    dropout: 0.1
+
+# Dense temporal (stride 1 -> 13 windows -> 52 tubes/seq with 2x2 spatial).
+local_branch_temporal_stride1:
+  grid_size: 2
+  cell_size: 112
+  tube_length: 4
+  temporal_stride: 1
+  encoder:
+    t_kernel: 2
+    h_patch: 14
+    w_patch: 14
+    embed_dim: 384
+    num_layers: 2
+    num_heads: 6
+    ffn_dim: 1536
+    dropout: 0.1
+
+# Longer tubes, fewer windows (tube_length=8, stride=4 -> 3 windows -> 12 tubes/seq).
+local_branch_temporal_len8:
+  grid_size: 2
+  cell_size: 112
+  tube_length: 8
+  temporal_stride: 4
+  encoder:
+    t_kernel: 2
+    h_patch: 14
+    w_patch: 14
+    embed_dim: 384
+    num_layers: 2
+    num_heads: 6
+    ffn_dim: 1536
+    dropout: 0.1
+
+train_dinov2_multiscale_spatial_4x4:
+  <<: *train_defaults
+  finetune: true
+  finetune_last_n_blocks: 1
+  backbone_lr: 0.00001
+  local_branch_section: local_branch_spatial_4x4
+
+train_dinov2_multiscale_spatial_8x8:
+  <<: *train_defaults
+  finetune: true
+  finetune_last_n_blocks: 1
+  backbone_lr: 0.00001
+  local_branch_section: local_branch_spatial_8x8
+
+train_dinov2_multiscale_temporal_stride1:
+  <<: *train_defaults
+  finetune: true
+  finetune_last_n_blocks: 1
+  backbone_lr: 0.00001
+  local_branch_section: local_branch_temporal_stride1
+
+train_dinov2_multiscale_temporal_len8:
+  <<: *train_defaults
+  finetune: true
+  finetune_last_n_blocks: 1
+  backbone_lr: 0.00001
+  local_branch_section: local_branch_temporal_len8
+
+# ---------------------------------------------------------------------------
+# Ablation: full model MINUS the temporal module (global DINOv2 branch).
+#
+# Identical local_branch + fusion modules/hyperparameters as the default model;
+# the global DINOv2 sequence branch is removed and its context vector into the
+# fusion cross-attention is replaced by a learnable query token. Isolates the
+# contribution of the global temporal context. Same 2x2 geometry as default.
+# ---------------------------------------------------------------------------
+train_ablation_no_temporal:
+  <<: *train_defaults
+  local_branch_section: local_branch
+  query_dim: 384
+
+# Ablation: remove the SPATIAL module (local tube branch). Global branch only —
+# DINOv2 per-frame + transformer aggregator -> MLP head. Keeps the global branch
+# identical to the full model (incl. last-block fine-tuning).
+train_ablation_no_spatial:
+  <<: *train_defaults
+  ablation_variant: no_spatial
+  finetune: true
+  finetune_last_n_blocks: 1
+  backbone_lr: 0.00001
+
+# Ablation: replace the cross-attention FUSION with weighted means. Both branches
+# kept identical to the full model; only the fusion module changes.
+train_ablation_weighted_mean:
+  <<: *train_defaults
+  ablation_variant: weighted_mean
+  finetune: true
+  finetune_last_n_blocks: 1
+  backbone_lr: 0.00001
+
+# ---------------------------------------------------------------------------
+# Temporal-module comparison — vary ONLY the global-branch aggregator, in the
+# global-only (no_spatial) setting so the temporal module is isolated. The
+# Transformer baseline is `train_ablation_no_spatial` above. See
+# TEMPORAL_COMPARISON.md.
+# ---------------------------------------------------------------------------
+_temporal_defaults: &temporal_defaults
+  <<: *train_defaults
+  ablation_variant: no_spatial
+  finetune: true
+  finetune_last_n_blocks: 1
+  backbone_lr: 0.00001
+
+train_temporal_lstm:
+  <<: *temporal_defaults
+  aggregator_kind: lstm
+
+train_temporal_gru:
+  <<: *temporal_defaults
+  aggregator_kind: gru
+
+train_temporal_mlp:
+  <<: *temporal_defaults
+  aggregator_kind: mlp
+
+train_temporal_conv1d:
+  <<: *temporal_defaults
+  aggregator_kind: conv1d
+
+train_temporal_linear_wavg:
+  <<: *temporal_defaults
+  aggregator_kind: linear_wavg
+
+# ---------------------------------------------------------------------------
+# Spatial-module comparison — vary ONLY the per-tube encoder, in the local-only
+# (no_temporal) setting so the spatial module is isolated. The tubelet-
+# transformer baseline is `train_ablation_no_temporal` above. See
+# SPATIAL_COMPARISON.md.
+# ---------------------------------------------------------------------------
+_spatial_defaults: &spatial_defaults
+  <<: *train_defaults
+  ablation_variant: no_temporal
+  local_branch_section: local_branch
+  query_dim: 384
+
+train_spatial_resnet3d:
+  <<: *spatial_defaults
+  encoder_kind: resnet3d
+  batch_size: 8          # r3d_18 over 28 tubes is memory-heavy
+
+train_spatial_vivit:
+  <<: *spatial_defaults
+  encoder_kind: vivit
+
+train_spatial_convlstm:
+  <<: *spatial_defaults
+  encoder_kind: convlstm
+
+train_spatial_tsm:
+  <<: *spatial_defaults
+  encoder_kind: tsm
+
+# ---------------------------------------------------------------------------
+# Augmentation (RGB only)
+# ---------------------------------------------------------------------------
+augment:
+  enabled: true
+  spatial:
+    flip_prob: 0.5
+    rotation_deg: 5.0
+    scale_range: [0.9, 1.1]
+    translate_frac: 0.05
+  photometric:
+    brightness_range: [0.8, 1.2]
+    contrast_range: [0.8, 1.2]
+    saturation_range: [0.8, 1.2]
+  temporal:
+    subseq_prob: 0.5
+    subseq_min_len: 4
+    stride_prob: 0.25
+    frame_drop_prob: 0.15
+    min_valid_after_drop: 4
diff --git a/experiments/temporal-models/tube-multiscale-fusion/pyproject.toml b/experiments/temporal-models/tube-multiscale-fusion/pyproject.toml
new file mode 100644
index 00000000..d05d333c
--- /dev/null
+++ b/experiments/temporal-models/tube-multiscale-fusion/pyproject.toml
@@ -0,0 +1,61 @@
+[build-system]
+requires = ["hatchling"]
+build-backend = "hatchling.build"
+
+[tool.hatch.build.targets.wheel]
+packages = ["src/tube_multiscale_fusion"]
+
+[project]
+name = "tube-multiscale-fusion"
+version = "0.1.0"
+description = "Two-branch temporal smoke classifier: global DINOv2 sequence context + local spatio-temporal tube transformer, fused via cross-attention"
+requires-python = ">=3.11"
+dependencies = [
+    "pyrocore",
+    "bbox-tube-temporal-core",
+    "lightning>=2.2",
+    "matplotlib>=3.8",
+    "numpy>=1.26,<2",
+    # Serving-path detector companion (tube_multiscale_fusion.model / package):
+    # ultralytics is imported directly; opencv-python-headless pins the cv2
+    # build that loads in headless envs (full opencv-python fails on libGL).
+    "ultralytics>=8.3",
+    "opencv-python-headless>=4.8",
+    "pyyaml>=6.0",
+    "scikit-learn>=1.4",
+    "tensorboard>=2.16",
+    "timm>=1.0",
+    "torch>=2.2",
+    "torchvision>=0.17",
+    "tqdm>=4.66",
+    "huggingface-hub>=0.24",
+]
+
+[tool.uv.sources]
+pyrocore = { path = "../../../lib/pyrocore" }
+bbox-tube-temporal-core = { path = "../../../lib/bbox-tube-temporal" }
+
+[dependency-groups]
+dev = [
+    "dvc[s3]>=3.56",
+    "jupyterlab>=4.0",
+    "nbqa>=1.9",
+    "nbstripout>=0.8",
+    "pytest>=8.0",
+    "ruff>=0.9",
+]
+
+[tool.ruff]
+target-version = "py311"
+line-length = 88
+
+[tool.ruff.lint]
+select = ["E", "F", "I", "W", "UP", "B", "SIM", "PLC0415"]
+
+[tool.ruff.format]
+quote-style = "double"
+
+[tool.pytest.ini_options]
+markers = [
+    "slow: tests that download large model weights (deselect with -m 'not slow')",
+]
diff --git a/experiments/temporal-models/tube-multiscale-fusion/scripts/.gitkeep b/experiments/temporal-models/tube-multiscale-fusion/scripts/.gitkeep
new file mode 100644
index 00000000..e69de29b
diff --git a/experiments/temporal-models/tube-multiscale-fusion/scripts/benchmark.py b/experiments/temporal-models/tube-multiscale-fusion/scripts/benchmark.py
new file mode 100644
index 00000000..22e02201
--- /dev/null
+++ b/experiments/temporal-models/tube-multiscale-fusion/scripts/benchmark.py
@@ -0,0 +1,227 @@
+"""Benchmark a trained tube-multiscale-fusion checkpoint.
+
+Computes the standardized Pyronear R&D metrics (see experiments/GUIDELINES.md):
+
+  * Recall @ FPR     -- tube-level recall at fixed false-positive rates
+  * Time-to-detection -- frames/seconds from tube start to first positive
+                         classification, via prefix masking
+  * Inference latency -- milliseconds per sequence and per frame (GPU + CPU)
+  * Model size        -- trainable / total parameter count and FLOPs
+
+Writes a JSON report and prints a human-readable summary.
+
+Example:
+    uv run python scripts/benchmark.py \\
+        --data-dir data/05_model_input/val \\
+        --checkpoint data/06_models/dinov2_multiscale/best_checkpoint.pt \\
+        --predictions data/08_reporting/dinov2_multiscale/val/predictions.json \\
+        --output data/08_reporting/dinov2_multiscale/val/benchmark.json
+"""
+
+import argparse
+import json
+import re
+import time
+from datetime import datetime
+from pathlib import Path
+
+import numpy as np
+import torch
+from sklearn.metrics import roc_curve
+from torch.utils.flop_counter import FlopCounterMode
+
+from tube_multiscale_fusion.dataset import TubePatchDataset
+from tube_multiscale_fusion.lit_module import LitTubeMultiscaleClassifier
+
+_TS_RE = re.compile(r"(\d{4}-\d{2}-\d{2}T\d{2}-\d{2}-\d{2})")
+
+
+def _parse_ts(frame_id: str) -> datetime | None:
+    m = _TS_RE.search(frame_id)
+    if not m:
+        return None
+    try:
+        return datetime.strptime(m.group(1), "%Y-%m-%dT%H-%M-%S")
+    except ValueError:
+        return None
+
+
+def recall_at_fpr(predictions: list[dict], fprs: list[float]) -> dict[str, float]:
+    """Tube-level recall at fixed false-positive rates from saved probabilities."""
+    labels = np.array([p["truth"] for p in predictions])
+    probs = np.array([p["prob"] for p in predictions])
+    if labels.sum() == 0 or labels.sum() == len(labels):
+        return {f"recall@fpr={f}": float("nan") for f in fprs}
+    fpr, tpr, _ = roc_curve(labels, probs)
+    out: dict[str, float] = {}
+    for target in fprs:
+        # Highest TPR achievable without exceeding the target FPR.
+        feasible = tpr[fpr <= target]
+        out[f"recall@fpr={target}"] = float(feasible.max()) if len(feasible) else 0.0
+    return out
+
+
+def median_frame_dt_seconds(ds: TubePatchDataset) -> float:
+    """Median inter-frame interval across all sequences, from frame-id timestamps."""
+    deltas: list[float] = []
+    for rec in ds.index:
+        meta = json.loads((ds.split_dir / rec["sequence_id"] / "meta.json").read_text())
+        ts = [_parse_ts(f["frame_id"]) for f in meta["frames"]]
+        ts = [t for t in ts if t is not None]
+        for a, b in zip(ts, ts[1:], strict=False):
+            deltas.append((b - a).total_seconds())
+    return float(np.median(deltas)) if deltas else float("nan")
+
+
+@torch.no_grad()
+def time_to_detection(
+    lit: LitTubeMultiscaleClassifier,
+    ds: TubePatchDataset,
+    device: torch.device,
+    dt_seconds: float,
+    threshold: float = 0.5,
+) -> dict[str, float]:
+    """For each positive tube, smallest prefix length whose prob >= threshold.
+
+    Feeds increasing prefixes (k = 2..T frames) by masking later frames, exactly
+    the variable-length contract the model was trained with. The reported time is
+    ``(k_fire - 1) * dt`` -- elapsed wall-clock from the first tube frame to the
+    frame at which the classifier first crosses the threshold.
+    """
+    lit.eval()
+    first_fire_frames: list[int] = []
+    never_fired = 0
+    n_pos = 0
+    for i in range(len(ds)):
+        item = ds[i]
+        if int(item["label"].item()) != 1:
+            continue
+        n_pos += 1
+        patches = item["patches"].unsqueeze(0).to(device)
+        base_mask = item["mask"]
+        n_valid = int(base_mask.sum().item())
+        fired_at = None
+        for k in range(2, n_valid + 1):
+            mask_k = torch.zeros_like(base_mask)
+            mask_k[:k] = True
+            prob = torch.sigmoid(lit(patches, mask_k.unsqueeze(0).to(device))).item()
+            if prob >= threshold:
+                fired_at = k
+                break
+        if fired_at is None:
+            never_fired += 1
+        else:
+            first_fire_frames.append(fired_at)
+    frames = np.array(first_fire_frames) if first_fire_frames else np.array([np.nan])
+    return {
+        "n_positive": n_pos,
+        "n_detected": len(first_fire_frames),
+        "n_never_fired": never_fired,
+        "median_frames_to_detect": float(np.median(frames)),
+        "mean_frames_to_detect": float(np.mean(frames)),
+        "median_seconds_to_detect": float(np.median(frames) - 1) * dt_seconds,
+        "mean_seconds_to_detect": float(np.mean(frames) - 1) * dt_seconds,
+        "frame_dt_seconds": dt_seconds,
+    }
+
+
+def model_size(lit: LitTubeMultiscaleClassifier, device: torch.device) -> dict:
+    total = sum(p.numel() for p in lit.model.parameters())
+    trainable = sum(p.numel() for p in lit.model.parameters() if p.requires_grad)
+    x = torch.randn(1, 16, 3, 224, 224, device=device)
+    mask = torch.ones(1, 16, dtype=torch.bool, device=device)
+    lit.eval()
+    with FlopCounterMode(display=False) as fcm:
+        lit(x, mask)
+    total_flops = fcm.get_total_flops()
+    return {
+        "total_params": int(total),
+        "total_params_millions": round(total / 1e6, 2),
+        "trainable_params": int(trainable),
+        "trainable_params_millions": round(trainable / 1e6, 2),
+        "frozen_params_millions": round((total - trainable) / 1e6, 2),
+        "flops_per_sequence": int(total_flops),
+        "gflops_per_sequence": round(total_flops / 1e9, 1),
+        "gflops_per_frame": round(total_flops / 1e9 / 16, 2),
+    }
+
+
+@torch.no_grad()
+def latency(
+    lit: LitTubeMultiscaleClassifier,
+    device: torch.device,
+    n_warmup: int = 5,
+    n_iters: int = 30,
+) -> dict[str, float]:
+    lit.eval()
+    x = torch.randn(1, 16, 3, 224, 224, device=device)
+    mask = torch.ones(1, 16, dtype=torch.bool, device=device)
+    for _ in range(n_warmup):
+        lit(x, mask)
+    if device.type == "cuda":
+        torch.cuda.synchronize()
+    times: list[float] = []
+    for _ in range(n_iters):
+        t0 = time.perf_counter()
+        lit(x, mask)
+        if device.type == "cuda":
+            torch.cuda.synchronize()
+        times.append((time.perf_counter() - t0) * 1000.0)
+    arr = np.array(times)
+    return {
+        "ms_per_sequence_mean": float(arr.mean()),
+        "ms_per_sequence_std": float(arr.std()),
+        "ms_per_frame_mean": float(arr.mean() / 16),
+    }
+
+
+def main() -> None:
+    parser = argparse.ArgumentParser(description=__doc__)
+    parser.add_argument("--data-dir", type=Path, required=True)
+    parser.add_argument("--checkpoint", type=Path, required=True)
+    parser.add_argument("--predictions", type=Path, required=True)
+    parser.add_argument("--output", type=Path, required=True)
+    parser.add_argument("--max-frames", type=int, default=16)
+    args = parser.parse_args()
+
+    predictions = json.loads(args.predictions.read_text())
+    ds = TubePatchDataset(args.data_dir, max_frames=args.max_frames)
+
+    lit = LitTubeMultiscaleClassifier.load_from_checkpoint(
+        str(args.checkpoint), pretrained=False
+    )
+    lit.eval()
+
+    report: dict = {"hardware": {}}
+    gpu = torch.cuda.is_available()
+    report["hardware"]["gpu"] = torch.cuda.get_device_name(0) if gpu else None
+    report["hardware"]["cpu_threads"] = torch.get_num_threads()
+
+    print("Computing recall @ FPR ...")
+    report["recall_at_fpr"] = recall_at_fpr(predictions, [0.01, 0.05, 0.1])
+
+    print("Computing model size + FLOPs ...")
+    size_device = torch.device("cuda" if gpu else "cpu")
+    lit.to(size_device)
+    report["model_size"] = model_size(lit, size_device)
+
+    print("Computing time-to-detection (prefix masking) ...")
+    dt = median_frame_dt_seconds(ds)
+    report["time_to_detection"] = time_to_detection(lit, ds, size_device, dt)
+
+    print("Measuring GPU latency ..." if gpu else "Skipping GPU latency (no CUDA)")
+    if gpu:
+        lit.to(torch.device("cuda"))
+        report["latency_gpu"] = latency(lit, torch.device("cuda"))
+
+    print("Measuring CPU latency ...")
+    lit.to(torch.device("cpu"))
+    report["latency_cpu"] = latency(lit, torch.device("cpu"))
+
+    args.output.parent.mkdir(parents=True, exist_ok=True)
+    args.output.write_text(json.dumps(report, indent=2))
+    print(json.dumps(report, indent=2))
+
+
+if __name__ == "__main__":
+    main()
diff --git a/experiments/temporal-models/tube-multiscale-fusion/scripts/build_model_input.py b/experiments/temporal-models/tube-multiscale-fusion/scripts/build_model_input.py
new file mode 100644
index 00000000..45ab94dd
--- /dev/null
+++ b/experiments/temporal-models/tube-multiscale-fusion/scripts/build_model_input.py
@@ -0,0 +1,118 @@
+"""Crop tube bboxes from raw frames into 224x224 PNG patches per tube.
+
+For each tube JSON in ``--tubes-dir``, look up the source sequence
+under ``--raw-dir`` and write a directory of cropped PNG frames + a
+``meta.json`` to ``--output-dir/<sequence_id>/``. Also writes a
+``_index.json`` listing all surviving tubes.
+
+Wipes ``--output-dir`` at the start so stale outputs don't linger.
+"""
+
+import argparse
+import json
+import os
+import shutil
+from concurrent.futures import ProcessPoolExecutor, as_completed
+from pathlib import Path
+
+from bbox_tube_temporal.model_input import LABEL_TO_INT, process_tube
+
+
+def _process_one(
+    tube_path: Path,
+    raw_dir: Path,
+    out_dir: Path,
+    context_factor: float,
+    patch_size: int,
+) -> tuple[str | None, str, str | None]:
+    """Worker: returns (sequence_id, label, error_or_none)."""
+    try:
+        record = json.loads(tube_path.read_text())
+        sequence_id = record["sequence_id"]
+        label = record["label"]
+        process_tube(
+            tube_path=tube_path,
+            raw_dir=raw_dir,
+            out_dir=out_dir,
+            context_factor=context_factor,
+            patch_size=patch_size,
+        )
+        return sequence_id, label, None
+    except Exception as exc:  # noqa: BLE001
+        return None, "", f"{tube_path.name}: {exc}"
+
+
+def main() -> None:
+    parser = argparse.ArgumentParser(description=__doc__)
+    parser.add_argument("--tubes-dir", type=Path, required=True)
+    parser.add_argument("--raw-dir", type=Path, required=True)
+    parser.add_argument("--output-dir", type=Path, required=True)
+    parser.add_argument("--context-factor", type=float, required=True)
+    parser.add_argument("--patch-size", type=int, required=True)
+    parser.add_argument(
+        "--workers",
+        type=int,
+        default=0,
+        help="Number of worker processes (0 = os.cpu_count()).",
+    )
+    args = parser.parse_args()
+
+    workers = args.workers if args.workers > 0 else (os.cpu_count() or 1)
+
+    if args.output_dir.exists():
+        shutil.rmtree(args.output_dir)
+    args.output_dir.mkdir(parents=True, exist_ok=True)
+
+    tube_paths = sorted(
+        p for p in args.tubes_dir.glob("*.json") if p.name != "_summary.json"
+    )
+
+    index: list[dict] = []
+    errors: list[str] = []
+    with ProcessPoolExecutor(max_workers=workers) as pool:
+        futures = [
+            pool.submit(
+                _process_one,
+                p,
+                args.raw_dir,
+                args.output_dir,
+                args.context_factor,
+                args.patch_size,
+            )
+            for p in tube_paths
+        ]
+        for fut in as_completed(futures):
+            sequence_id, label, err = fut.result()
+            if err is not None:
+                errors.append(err)
+                continue
+            assert sequence_id is not None
+            num_frames = len(
+                json.loads((args.output_dir / sequence_id / "meta.json").read_text())[
+                    "frames"
+                ]
+            )
+            index.append(
+                {
+                    "sequence_id": sequence_id,
+                    "label_int": LABEL_TO_INT[label],
+                    "num_frames": num_frames,
+                }
+            )
+
+    index.sort(key=lambda r: r["sequence_id"])
+    (args.output_dir / "_index.json").write_text(json.dumps(index, indent=2))
+
+    split = args.tubes_dir.name
+    print(
+        f"[{split}] wrote {len(index)}/{len(tube_paths)} tubes "
+        f"with {workers} workers (errors={len(errors)})"
+    )
+    for e in errors[:5]:
+        print(f"  error: {e}")
+    if len(errors) > 5:
+        print(f"  ... and {len(errors) - 5} more")
+
+
+if __name__ == "__main__":
+    main()
diff --git a/experiments/temporal-models/tube-multiscale-fusion/scripts/build_tubes.py b/experiments/temporal-models/tube-multiscale-fusion/scripts/build_tubes.py
new file mode 100644
index 00000000..96dc7b5b
--- /dev/null
+++ b/experiments/temporal-models/tube-multiscale-fusion/scripts/build_tubes.py
@@ -0,0 +1,195 @@
+"""Build per-sequence smoke tube JSON dataset from label .txt files.
+
+For each sequence under ``--input-dir/{wildfire,fp}/``:
+
+1. Load detections from labels (5-col GT for wildfire, 6-col YOLO for fp).
+2. Build candidate tubes via greedy IoU matching.
+3. Select the longest tube; tie-break by non-gap entries.
+4. Geometrically interpolate gap bboxes.
+5. Apply length / observation filters.
+6. Write a JSON file per surviving sequence and a summary file with
+   per-split stats and dropped-sequence reasons.
+
+No YOLO inference is performed -- the labels carry everything we need.
+"""
+
+import argparse
+import json
+from dataclasses import dataclass
+from pathlib import Path
+
+from bbox_tube_temporal.data import (
+    is_wf_sequence,
+    list_sequences,
+    load_frame_detections,
+)
+from bbox_tube_temporal.tubes import (
+    build_tubes,
+    interpolate_gaps,
+    select_longest_tube,
+)
+from bbox_tube_temporal.types import Tube
+
+
+@dataclass
+class DropRecord:
+    sequence_id: str
+    reason: str
+
+
+def _serialize_tube(
+    *,
+    sequence_id: str,
+    split: str,
+    label: str,
+    source: str,
+    num_frames: int,
+    tube: Tube,
+    frame_id_by_idx: dict[int, str],
+) -> dict:
+    return {
+        "sequence_id": sequence_id,
+        "split": split,
+        "label": label,
+        "source": source,
+        "num_frames": num_frames,
+        "tube": {
+            "start_frame": tube.start_frame,
+            "end_frame": tube.end_frame,
+            "entries": [
+                {
+                    "frame_idx": e.frame_idx,
+                    "frame_id": frame_id_by_idx.get(e.frame_idx, ""),
+                    "bbox": [
+                        e.detection.cx,
+                        e.detection.cy,
+                        e.detection.w,
+                        e.detection.h,
+                    ]
+                    if e.detection is not None
+                    else None,
+                    "is_gap": e.is_gap,
+                    "confidence": e.detection.confidence
+                    if e.detection is not None
+                    else None,
+                }
+                for e in tube.entries
+            ],
+        },
+    }
+
+
+def _process_sequence(
+    seq_dir: Path,
+    *,
+    split: str,
+    iou_threshold: float,
+    max_misses: int,
+    min_tube_length: int,
+    min_detected_entries: int,
+) -> tuple[dict | None, str | None]:
+    """Process a single sequence.
+
+    Returns ``(record_or_None, drop_reason_or_None)``.
+    """
+    is_wf = is_wf_sequence(seq_dir)
+    label = "smoke" if is_wf else "fp"
+    source = "gt" if is_wf else "yolo"
+
+    if not (seq_dir / "labels").is_dir():
+        return None, "no_labels_dir"
+
+    fdets = load_frame_detections(seq_dir)
+    if not fdets:
+        return None, "no_frames"
+
+    total_dets = sum(len(fd.detections) for fd in fdets)
+    if total_dets < min_detected_entries:
+        return None, "no_detections"
+
+    tubes = build_tubes(fdets, iou_threshold=iou_threshold, max_misses=max_misses)
+    if not tubes:
+        return None, "no_tubes"
+
+    selected = select_longest_tube(tubes)
+    assert selected is not None  # tubes is non-empty
+
+    length = selected.end_frame - selected.start_frame + 1
+    if length < min_tube_length:
+        return None, "too_short"
+
+    n_observed = sum(1 for e in selected.entries if e.detection is not None)
+    if n_observed < min_detected_entries:
+        return None, "too_few_observed"
+
+    interpolate_gaps(selected)
+
+    frame_id_by_idx = {fd.frame_idx: fd.frame_id for fd in fdets}
+    record = _serialize_tube(
+        sequence_id=seq_dir.name,
+        split=split,
+        label=label,
+        source=source,
+        num_frames=len(fdets),
+        tube=selected,
+        frame_id_by_idx=frame_id_by_idx,
+    )
+    return record, None
+
+
+def main() -> None:
+    parser = argparse.ArgumentParser(description=__doc__)
+    parser.add_argument("--input-dir", type=Path, required=True)
+    parser.add_argument("--output-dir", type=Path, required=True)
+    parser.add_argument("--iou-threshold", type=float, default=0.2)
+    parser.add_argument("--max-misses", type=int, default=2)
+    parser.add_argument("--min-tube-length", type=int, default=4)
+    parser.add_argument("--min-detected-entries", type=int, default=2)
+    args = parser.parse_args()
+
+    split = args.input_dir.name
+    args.output_dir.mkdir(parents=True, exist_ok=True)
+
+    seq_dirs = list_sequences(args.input_dir)
+    written = 0
+    by_label: dict[str, int] = {"smoke": 0, "fp": 0}
+    dropped: list[DropRecord] = []
+
+    for seq_dir in seq_dirs:
+        record, reason = _process_sequence(
+            seq_dir,
+            split=split,
+            iou_threshold=args.iou_threshold,
+            max_misses=args.max_misses,
+            min_tube_length=args.min_tube_length,
+            min_detected_entries=args.min_detected_entries,
+        )
+        if reason is not None:
+            dropped.append(DropRecord(sequence_id=seq_dir.name, reason=reason))
+            continue
+
+        out_path = args.output_dir / f"{seq_dir.name}.json"
+        out_path.write_text(json.dumps(record, indent=2))
+        written += 1
+        by_label[record["label"]] += 1
+
+    summary = {
+        "split": split,
+        "total_sequences": len(seq_dirs),
+        "tubes_written": written,
+        "by_label": by_label,
+        "dropped": [
+            {"sequence_id": d.sequence_id, "reason": d.reason} for d in dropped
+        ],
+    }
+    (args.output_dir / "_summary.json").write_text(json.dumps(summary, indent=2))
+
+    print(
+        f"[{split}] wrote {written}/{len(seq_dirs)} tubes "
+        f"(smoke={by_label['smoke']}, fp={by_label['fp']}, "
+        f"dropped={len(dropped)})"
+    )
+
+
+if __name__ == "__main__":
+    main()
diff --git a/experiments/temporal-models/tube-multiscale-fusion/scripts/evaluate.py b/experiments/temporal-models/tube-multiscale-fusion/scripts/evaluate.py
new file mode 100644
index 00000000..2f159b7d
--- /dev/null
+++ b/experiments/temporal-models/tube-multiscale-fusion/scripts/evaluate.py
@@ -0,0 +1,157 @@
+"""Evaluate a trained tube-multiscale classifier on a split.
+
+Loads the best checkpoint, runs inference over the dataset, computes
+classification metrics + PR/ROC curves, and writes them under
+``--output-dir``.
+"""
+
+import argparse
+import json
+from pathlib import Path
+
+import lightning as L
+import numpy as np
+import torch
+import yaml
+from sklearn.metrics import (
+    average_precision_score,
+    confusion_matrix,
+    roc_auc_score,
+)
+from torch.utils.data import DataLoader
+
+from tube_multiscale_fusion.dataset import TubePatchDataset
+from tube_multiscale_fusion.eval_plots import (
+    plot_confusion_matrix,
+    plot_pr_curve,
+    plot_roc_curve,
+)
+from tube_multiscale_fusion.lit_module import LitTubeMultiscaleClassifier
+
+
+def main() -> None:
+    parser = argparse.ArgumentParser(description=__doc__)
+    parser.add_argument("--data-dir", type=Path, required=True)
+    parser.add_argument("--checkpoint", type=Path, required=True)
+    parser.add_argument("--output-dir", type=Path, required=True)
+    parser.add_argument("--params-path", type=Path, required=True)
+    parser.add_argument("--params-key", required=True)
+    args = parser.parse_args()
+
+    full_params = yaml.safe_load(args.params_path.read_text())
+    cfg = full_params[args.params_key]
+    args.output_dir.mkdir(parents=True, exist_ok=True)
+
+    L.seed_everything(cfg["seed"], workers=True)
+
+    lit = LitTubeMultiscaleClassifier.load_from_checkpoint(
+        str(args.checkpoint), pretrained=False
+    )
+    lit.eval()
+    device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
+    print(f"Using device: {device}")
+    lit.to(device)
+
+    ds = TubePatchDataset(args.data_dir, max_frames=cfg["max_frames"])
+    loader = DataLoader(
+        ds,
+        batch_size=cfg["batch_size"],
+        shuffle=False,
+        num_workers=cfg["num_workers"],
+    )
+
+    all_probs: list[float] = []
+    all_labels: list[float] = []
+    all_sequence_ids: list[str] = []
+    with torch.no_grad():
+        for batch in loader:
+            patches = batch["patches"].to(device)
+            mask = batch["mask"].to(device)
+            logits = lit(patches, mask)
+            probs = torch.sigmoid(logits).cpu().tolist()
+            all_probs.extend(probs)
+            all_labels.extend(batch["label"].tolist())
+            all_sequence_ids.extend(batch["sequence_id"])
+
+    probs = np.asarray(all_probs)
+    labels = np.asarray(all_labels)
+    preds = (probs > 0.5).astype(int)
+
+    cm = confusion_matrix(labels, preds, labels=[0, 1]).tolist()
+    tn, fp, fn, tp = cm[0][0], cm[0][1], cm[1][0], cm[1][1]
+    accuracy = (tp + tn) / max(tp + tn + fp + fn, 1)
+    precision = tp / max(tp + fp, 1)
+    recall = tp / max(tp + fn, 1)
+    f1 = 2 * precision * recall / max(precision + recall, 1e-8)
+    pr_auc = float(average_precision_score(labels, probs)) if labels.sum() > 0 else 0.0
+    roc_auc = (
+        float(roc_auc_score(labels, probs)) if 0 < labels.sum() < len(labels) else 0.0
+    )
+
+    neg_total = tn + fp
+    pos_total = tp + fn
+    fp_as_fp = tn / neg_total if neg_total > 0 else 0.0
+    fp_as_smoke = fp / neg_total if neg_total > 0 else 0.0
+    smoke_as_fp = fn / pos_total if pos_total > 0 else 0.0
+    smoke_as_smoke = tp / pos_total if pos_total > 0 else 0.0
+
+    metrics = {
+        "accuracy": accuracy,
+        "precision": precision,
+        "recall": recall,
+        "f1": f1,
+        "pr_auc": pr_auc,
+        "roc_auc": roc_auc,
+        "confusion_matrix": {"tn": tn, "fp": fp, "fn": fn, "tp": tp},
+        "confusion_matrix_normalized": {
+            "fp_as_fp": fp_as_fp,
+            "fp_as_smoke": fp_as_smoke,
+            "smoke_as_fp": smoke_as_fp,
+            "smoke_as_smoke": smoke_as_smoke,
+        },
+        "n_samples": int(len(labels)),
+        "n_positive": int(labels.sum()),
+    }
+    (args.output_dir / "metrics.json").write_text(json.dumps(metrics, indent=2))
+
+    split_name = args.output_dir.parent.name
+    cm_abs = np.array([[tn, fp], [fn, tp]], dtype=float)
+    cm_norm = np.array(
+        [[fp_as_fp, fp_as_smoke], [smoke_as_fp, smoke_as_smoke]], dtype=float
+    )
+    plot_confusion_matrix(
+        cm_abs,
+        args.output_dir / "confusion_matrix.png",
+        title=f"tube-multiscale / {split_name} (counts)",
+        normalized=False,
+    )
+    plot_confusion_matrix(
+        cm_norm,
+        args.output_dir / "confusion_matrix_normalized.png",
+        title=f"tube-multiscale / {split_name} (row-normalized)",
+        normalized=True,
+    )
+
+    plot_pr_curve(labels, probs, args.output_dir / "pr_curve.png", title="PR")
+    plot_roc_curve(labels, probs, args.output_dir / "roc_curve.png", title="ROC")
+
+    predictions = [
+        {
+            "sequence_id": seq_id,
+            "truth": int(truth),
+            "prob": float(prob),
+            "predicted": int(pred),
+            "correct": bool(int(truth) == int(pred)),
+        }
+        for seq_id, truth, prob, pred in zip(
+            all_sequence_ids, labels, probs, preds, strict=True
+        )
+    ]
+    predictions.sort(key=lambda r: r["prob"], reverse=True)
+    (args.output_dir / "predictions.json").write_text(json.dumps(predictions, indent=2))
+
+    print(json.dumps(metrics, indent=2))
+
+
+if __name__ == "__main__":
+    main()
diff --git a/experiments/temporal-models/tube-multiscale-fusion/scripts/evaluate_ablation.py b/experiments/temporal-models/tube-multiscale-fusion/scripts/evaluate_ablation.py
new file mode 100644
index 00000000..88be5417
--- /dev/null
+++ b/experiments/temporal-models/tube-multiscale-fusion/scripts/evaluate_ablation.py
@@ -0,0 +1,154 @@
+"""Evaluate the no-temporal-module ablation classifier on a split.
+
+Same metric/plot outputs as ``scripts/evaluate.py`` so the ablation is
+directly comparable to the full model.
+"""
+
+import argparse
+import json
+from pathlib import Path
+
+import lightning as L
+import numpy as np
+import torch
+import yaml
+from sklearn.metrics import (
+    average_precision_score,
+    confusion_matrix,
+    roc_auc_score,
+)
+from torch.utils.data import DataLoader
+
+from tube_multiscale_fusion.dataset import TubePatchDataset
+from tube_multiscale_fusion.eval_plots import (
+    plot_confusion_matrix,
+    plot_pr_curve,
+    plot_roc_curve,
+)
+from tube_multiscale_fusion.lit_ablation import LitAblationNoTemporal
+
+
+def main() -> None:
+    parser = argparse.ArgumentParser(description=__doc__)
+    parser.add_argument("--data-dir", type=Path, required=True)
+    parser.add_argument("--checkpoint", type=Path, required=True)
+    parser.add_argument("--output-dir", type=Path, required=True)
+    parser.add_argument("--params-path", type=Path, required=True)
+    parser.add_argument("--params-key", required=True)
+    args = parser.parse_args()
+
+    full_params = yaml.safe_load(args.params_path.read_text())
+    cfg = full_params[args.params_key]
+    args.output_dir.mkdir(parents=True, exist_ok=True)
+
+    L.seed_everything(cfg["seed"], workers=True)
+
+    lit = LitAblationNoTemporal.load_from_checkpoint(str(args.checkpoint))
+    lit.eval()
+    device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
+    print(f"Using device: {device}")
+    lit.to(device)
+
+    ds = TubePatchDataset(args.data_dir, max_frames=cfg["max_frames"])
+    loader = DataLoader(
+        ds, batch_size=cfg["batch_size"], shuffle=False, num_workers=cfg["num_workers"]
+    )
+
+    all_probs: list[float] = []
+    all_labels: list[float] = []
+    all_sequence_ids: list[str] = []
+    with torch.no_grad():
+        for batch in loader:
+            patches = batch["patches"].to(device)
+            mask = batch["mask"].to(device)
+            logits = lit(patches, mask)
+            all_probs.extend(torch.sigmoid(logits).cpu().tolist())
+            all_labels.extend(batch["label"].tolist())
+            all_sequence_ids.extend(batch["sequence_id"])
+
+    probs = np.asarray(all_probs)
+    labels = np.asarray(all_labels)
+    preds = (probs > 0.5).astype(int)
+
+    cm = confusion_matrix(labels, preds, labels=[0, 1]).tolist()
+    tn, fp, fn, tp = cm[0][0], cm[0][1], cm[1][0], cm[1][1]
+    accuracy = (tp + tn) / max(tp + tn + fp + fn, 1)
+    precision = tp / max(tp + fp, 1)
+    recall = tp / max(tp + fn, 1)
+    f1 = 2 * precision * recall / max(precision + recall, 1e-8)
+    pr_auc = float(average_precision_score(labels, probs)) if labels.sum() > 0 else 0.0
+    roc_auc = (
+        float(roc_auc_score(labels, probs)) if 0 < labels.sum() < len(labels) else 0.0
+    )
+
+    neg_total = tn + fp
+    pos_total = tp + fn
+    metrics = {
+        "accuracy": accuracy,
+        "precision": precision,
+        "recall": recall,
+        "f1": f1,
+        "pr_auc": pr_auc,
+        "roc_auc": roc_auc,
+        "confusion_matrix": {"tn": tn, "fp": fp, "fn": fn, "tp": tp},
+        "confusion_matrix_normalized": {
+            "fp_as_fp": tn / neg_total if neg_total > 0 else 0.0,
+            "fp_as_smoke": fp / neg_total if neg_total > 0 else 0.0,
+            "smoke_as_fp": fn / pos_total if pos_total > 0 else 0.0,
+            "smoke_as_smoke": tp / pos_total if pos_total > 0 else 0.0,
+        },
+        "n_samples": int(len(labels)),
+        "n_positive": int(labels.sum()),
+    }
+    (args.output_dir / "metrics.json").write_text(json.dumps(metrics, indent=2))
+
+    split_name = args.output_dir.parent.name
+    cm_abs = np.array([[tn, fp], [fn, tp]], dtype=float)
+    cm_norm = np.array(
+        [
+            [
+                metrics["confusion_matrix_normalized"]["fp_as_fp"],
+                metrics["confusion_matrix_normalized"]["fp_as_smoke"],
+            ],
+            [
+                metrics["confusion_matrix_normalized"]["smoke_as_fp"],
+                metrics["confusion_matrix_normalized"]["smoke_as_smoke"],
+            ],
+        ],
+        dtype=float,
+    )
+    plot_confusion_matrix(
+        cm_abs,
+        args.output_dir / "confusion_matrix.png",
+        title=f"ablation-no-temporal / {split_name} (counts)",
+        normalized=False,
+    )
+    plot_confusion_matrix(
+        cm_norm,
+        args.output_dir / "confusion_matrix_normalized.png",
+        title=f"ablation-no-temporal / {split_name} (row-normalized)",
+        normalized=True,
+    )
+    plot_pr_curve(labels, probs, args.output_dir / "pr_curve.png", title="PR")
+    plot_roc_curve(labels, probs, args.output_dir / "roc_curve.png", title="ROC")
+
+    predictions = [
+        {
+            "sequence_id": seq_id,
+            "truth": int(truth),
+            "prob": float(prob),
+            "predicted": int(pred),
+            "correct": bool(int(truth) == int(pred)),
+        }
+        for seq_id, truth, prob, pred in zip(
+            all_sequence_ids, labels, probs, preds, strict=True
+        )
+    ]
+    predictions.sort(key=lambda r: r["prob"], reverse=True)
+    (args.output_dir / "predictions.json").write_text(json.dumps(predictions, indent=2))
+
+    print(json.dumps(metrics, indent=2))
+
+
+if __name__ == "__main__":
+    main()
diff --git a/experiments/temporal-models/tube-multiscale-fusion/scripts/evaluate_ablation_global.py b/experiments/temporal-models/tube-multiscale-fusion/scripts/evaluate_ablation_global.py
new file mode 100644
index 00000000..e2794f12
--- /dev/null
+++ b/experiments/temporal-models/tube-multiscale-fusion/scripts/evaluate_ablation_global.py
@@ -0,0 +1,154 @@
+"""Evaluate a global-branch ablation variant (no_spatial / weighted_mean).
+
+Same metric/plot outputs as ``scripts/evaluate.py`` for direct comparability.
+"""
+
+import argparse
+import json
+from pathlib import Path
+
+import lightning as L
+import numpy as np
+import torch
+import yaml
+from sklearn.metrics import (
+    average_precision_score,
+    confusion_matrix,
+    roc_auc_score,
+)
+from torch.utils.data import DataLoader
+
+from tube_multiscale_fusion.dataset import TubePatchDataset
+from tube_multiscale_fusion.eval_plots import (
+    plot_confusion_matrix,
+    plot_pr_curve,
+    plot_roc_curve,
+)
+from tube_multiscale_fusion.lit_ablation import LitAblationGlobal
+
+
+def main() -> None:
+    parser = argparse.ArgumentParser(description=__doc__)
+    parser.add_argument("--data-dir", type=Path, required=True)
+    parser.add_argument("--checkpoint", type=Path, required=True)
+    parser.add_argument("--output-dir", type=Path, required=True)
+    parser.add_argument("--params-path", type=Path, required=True)
+    parser.add_argument("--params-key", required=True)
+    args = parser.parse_args()
+
+    full_params = yaml.safe_load(args.params_path.read_text())
+    cfg = full_params[args.params_key]
+    args.output_dir.mkdir(parents=True, exist_ok=True)
+    variant = cfg["ablation_variant"]
+
+    L.seed_everything(cfg["seed"], workers=True)
+
+    lit = LitAblationGlobal.load_from_checkpoint(str(args.checkpoint), pretrained=False)
+    lit.eval()
+    device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
+    print(f"Using device: {device}")
+    lit.to(device)
+
+    ds = TubePatchDataset(args.data_dir, max_frames=cfg["max_frames"])
+    loader = DataLoader(
+        ds, batch_size=cfg["batch_size"], shuffle=False, num_workers=cfg["num_workers"]
+    )
+
+    all_probs: list[float] = []
+    all_labels: list[float] = []
+    all_sequence_ids: list[str] = []
+    with torch.no_grad():
+        for batch in loader:
+            patches = batch["patches"].to(device)
+            mask = batch["mask"].to(device)
+            logits = lit(patches, mask)
+            all_probs.extend(torch.sigmoid(logits).cpu().tolist())
+            all_labels.extend(batch["label"].tolist())
+            all_sequence_ids.extend(batch["sequence_id"])
+
+    probs = np.asarray(all_probs)
+    labels = np.asarray(all_labels)
+    preds = (probs > 0.5).astype(int)
+
+    cm = confusion_matrix(labels, preds, labels=[0, 1]).tolist()
+    tn, fp, fn, tp = cm[0][0], cm[0][1], cm[1][0], cm[1][1]
+    accuracy = (tp + tn) / max(tp + tn + fp + fn, 1)
+    precision = tp / max(tp + fp, 1)
+    recall = tp / max(tp + fn, 1)
+    f1 = 2 * precision * recall / max(precision + recall, 1e-8)
+    pr_auc = float(average_precision_score(labels, probs)) if labels.sum() > 0 else 0.0
+    roc_auc = (
+        float(roc_auc_score(labels, probs)) if 0 < labels.sum() < len(labels) else 0.0
+    )
+
+    neg_total = tn + fp
+    pos_total = tp + fn
+    metrics = {
+        "accuracy": accuracy,
+        "precision": precision,
+        "recall": recall,
+        "f1": f1,
+        "pr_auc": pr_auc,
+        "roc_auc": roc_auc,
+        "confusion_matrix": {"tn": tn, "fp": fp, "fn": fn, "tp": tp},
+        "confusion_matrix_normalized": {
+            "fp_as_fp": tn / neg_total if neg_total > 0 else 0.0,
+            "fp_as_smoke": fp / neg_total if neg_total > 0 else 0.0,
+            "smoke_as_fp": fn / pos_total if pos_total > 0 else 0.0,
+            "smoke_as_smoke": tp / pos_total if pos_total > 0 else 0.0,
+        },
+        "n_samples": int(len(labels)),
+        "n_positive": int(labels.sum()),
+    }
+    (args.output_dir / "metrics.json").write_text(json.dumps(metrics, indent=2))
+
+    split_name = args.output_dir.parent.name
+    cm_abs = np.array([[tn, fp], [fn, tp]], dtype=float)
+    cm_norm = np.array(
+        [
+            [
+                metrics["confusion_matrix_normalized"]["fp_as_fp"],
+                metrics["confusion_matrix_normalized"]["fp_as_smoke"],
+            ],
+            [
+                metrics["confusion_matrix_normalized"]["smoke_as_fp"],
+                metrics["confusion_matrix_normalized"]["smoke_as_smoke"],
+            ],
+        ],
+        dtype=float,
+    )
+    plot_confusion_matrix(
+        cm_abs,
+        args.output_dir / "confusion_matrix.png",
+        title=f"ablation-{variant} / {split_name} (counts)",
+        normalized=False,
+    )
+    plot_confusion_matrix(
+        cm_norm,
+        args.output_dir / "confusion_matrix_normalized.png",
+        title=f"ablation-{variant} / {split_name} (row-normalized)",
+        normalized=True,
+    )
+    plot_pr_curve(labels, probs, args.output_dir / "pr_curve.png", title="PR")
+    plot_roc_curve(labels, probs, args.output_dir / "roc_curve.png", title="ROC")
+
+    predictions = [
+        {
+            "sequence_id": seq_id,
+            "truth": int(truth),
+            "prob": float(prob),
+            "predicted": int(pred),
+            "correct": bool(int(truth) == int(pred)),
+        }
+        for seq_id, truth, prob, pred in zip(
+            all_sequence_ids, labels, probs, preds, strict=True
+        )
+    ]
+    predictions.sort(key=lambda r: r["prob"], reverse=True)
+    (args.output_dir / "predictions.json").write_text(json.dumps(predictions, indent=2))
+
+    print(json.dumps(metrics, indent=2))
+
+
+if __name__ == "__main__":
+    main()
diff --git a/experiments/temporal-models/tube-multiscale-fusion/scripts/package_model.py b/experiments/temporal-models/tube-multiscale-fusion/scripts/package_model.py
new file mode 100644
index 00000000..f61e4695
--- /dev/null
+++ b/experiments/temporal-models/tube-multiscale-fusion/scripts/package_model.py
@@ -0,0 +1,181 @@
+"""Package a trained tube-multiscale-fusion checkpoint into a deployable .zip.
+
+Builds a leaderboard-comparable model archive (mirroring the sibling
+``bbox-tube-temporal`` experiment): bundles the YOLO companion detector
+weights, the Lightning checkpoint, and the inference config needed by
+``TubeMultiscaleFusionModel`` to run the full raw-frames -> decision pipeline.
+
+The decision threshold is calibrated on the val patches to hit
+``package.target_recall`` before being pinned into the archive config.
+
+Example:
+    uv run python scripts/package_model.py \\
+        --checkpoint data/06_models/dinov2_multiscale/best_checkpoint.pt \\
+        --params-path params.yaml \\
+        --params-key train_dinov2_multiscale \\
+        --yolo-weights-path data/01_raw/models/best.pt \\
+        --val-patches-dir data/05_model_input/val \\
+        --output data/07_model_output/dinov2_multiscale/model_package.zip
+"""
+
+import argparse
+from pathlib import Path
+
+import numpy as np
+import torch
+import yaml
+from torch.utils.data import DataLoader
+
+from tube_multiscale_fusion.dataset import IMAGENET_MEAN, IMAGENET_STD, TubePatchDataset
+from tube_multiscale_fusion.package import _load_classifier, build_model_package
+
+
+def _calibrate_threshold(
+    probs: np.ndarray, labels: np.ndarray, *, target_recall: float
+) -> float:
+    """Smallest prob threshold whose recall on positives >= ``target_recall``.
+
+    Same rule as ``bbox_tube_temporal``'s packager (that helper lives in the
+    experiment, not the shared lib, so it's inlined here).
+    """
+    if not 0.0 < target_recall <= 1.0:
+        raise ValueError(f"target_recall must be in (0, 1], got {target_recall!r}")
+    pos_probs = np.sort(probs[labels == 1])
+    if pos_probs.size == 0:
+        raise ValueError("no positives in labels; cannot calibrate recall")
+    n_drop = int(np.floor(pos_probs.size * (1.0 - target_recall)))
+    return float(pos_probs[n_drop])
+
+
+def _collect_val_probabilities(
+    classifier: torch.nn.Module,
+    val_patches_dir: Path,
+    *,
+    max_frames: int,
+    batch_size: int,
+    num_workers: int,
+    device: torch.device,
+) -> tuple[np.ndarray, np.ndarray]:
+    """Run the classifier over val patches; return ``(probs, labels)`` arrays."""
+    ds = TubePatchDataset(val_patches_dir, max_frames=max_frames)
+    loader = DataLoader(
+        ds, batch_size=batch_size, shuffle=False, num_workers=num_workers
+    )
+    probs: list[float] = []
+    labels: list[float] = []
+    with torch.no_grad():
+        for batch in loader:
+            logits = classifier(batch["patches"].to(device), batch["mask"].to(device))
+            probs.extend(torch.sigmoid(logits).cpu().tolist())
+            labels.extend(batch["label"].tolist())
+    return np.asarray(probs), np.asarray(labels)
+
+
+def _build_config(
+    all_params: dict,
+    package_params: dict,
+    *,
+    max_frames: int,
+    threshold: float,
+) -> dict:
+    """Assemble the inference config embedded in the archive."""
+    return {
+        "infer": package_params["infer"],
+        "tubes": {
+            "iou_threshold": all_params["tubes"]["iou_threshold"],
+            "max_misses": all_params["tubes"]["max_misses"],
+            "min_tube_length": all_params["build_tubes"]["min_tube_length"],
+            "infer_min_tube_length": package_params["infer_min_tube_length"],
+            "min_detected_entries": all_params["build_tubes"]["min_detected_entries"],
+            "interpolate_gaps": True,
+        },
+        "model_input": {
+            "context_factor": all_params["model_input"]["context_factor"],
+            "patch_size": all_params["model_input"]["patch_size"],
+            "normalization": {
+                "mean": IMAGENET_MEAN.flatten().tolist(),
+                "std": IMAGENET_STD.flatten().tolist(),
+            },
+        },
+        "classifier": {"max_frames": max_frames},
+        "decision": {
+            "aggregation": "max_logit",
+            "threshold": float(threshold),
+            "target_recall": package_params["target_recall"],
+        },
+    }
+
+
+def main() -> None:
+    parser = argparse.ArgumentParser(description=__doc__)
+    parser.add_argument("--checkpoint", type=Path, required=True)
+    parser.add_argument("--params-path", type=Path, required=True)
+    parser.add_argument("--params-key", required=True)
+    parser.add_argument("--output", type=Path, required=True)
+    parser.add_argument(
+        "--yolo-weights-path",
+        type=Path,
+        default=Path("data/01_raw/models/best.pt"),
+    )
+    parser.add_argument(
+        "--val-patches-dir",
+        type=Path,
+        default=Path("data/05_model_input/val"),
+    )
+    args = parser.parse_args()
+
+    if not args.checkpoint.is_file():
+        raise FileNotFoundError(f"checkpoint not found: {args.checkpoint}")
+
+    all_params = yaml.safe_load(args.params_path.read_text())
+    if args.params_key not in all_params:
+        raise KeyError(f"params key not found in {args.params_path}: {args.params_key}")
+    if "package" not in all_params:
+        raise KeyError(f"'package' section missing from {args.params_path}")
+    train_cfg = all_params[args.params_key]
+    package_params = all_params["package"]
+
+    device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
+    classifier = _load_classifier(args.checkpoint).to(device)
+    max_frames = int(classifier.hparams["max_frames"])
+
+    probs, labels = _collect_val_probabilities(
+        classifier,
+        args.val_patches_dir,
+        max_frames=max_frames,
+        batch_size=train_cfg.get("batch_size", 32),
+        num_workers=train_cfg.get("num_workers", 4),
+        device=device,
+    )
+    threshold_prob = _calibrate_threshold(
+        probs, labels, target_recall=package_params["target_recall"]
+    )
+    # calibrate_threshold returns a probability; the max_logit rule compares raw
+    # logits, so convert via the inverse sigmoid (logit) once here. Clamp away
+    # from 0/1 so a saturated sigmoid doesn't produce a +/-inf threshold.
+    p = float(np.clip(threshold_prob, 1e-6, 1.0 - 1e-6))
+    threshold_logit = float(np.log(p / (1.0 - p)))
+
+    config = _build_config(
+        all_params,
+        package_params,
+        max_frames=max_frames,
+        threshold=threshold_logit,
+    )
+    build_model_package(
+        yolo_weights_path=args.yolo_weights_path,
+        classifier_ckpt_path=args.checkpoint,
+        config=config,
+        variant=args.params_key.removeprefix("train_"),
+        output_path=args.output,
+    )
+    print(
+        f"[package] wrote {args.output} "
+        f"({args.output.stat().st_size / 1e6:.1f} MB) | "
+        f"variant={args.params_key} target_recall={package_params['target_recall']} "
+        f"prob_threshold={threshold_prob:.4f} logit_threshold={threshold_logit:.4f}"
+    )
+
+
+if __name__ == "__main__":
+    main()
diff --git a/experiments/temporal-models/tube-multiscale-fusion/scripts/prepare.py b/experiments/temporal-models/tube-multiscale-fusion/scripts/prepare.py
new file mode 100644
index 00000000..8fc76433
--- /dev/null
+++ b/experiments/temporal-models/tube-multiscale-fusion/scripts/prepare.py
@@ -0,0 +1,66 @@
+"""Download YOLO model weights from HuggingFace Hub.
+
+Fetches a single model file from a HuggingFace repository and saves it
+to the local data directory. Skips the download if the file already exists.
+
+These weights are the serving-time detector companion: the training /
+data-prep pipeline reads ground-truth label files (no inference), but the
+``TemporalModel`` serving path (``tube_multiscale_fusion.model``) runs this
+YOLO over raw frames before building tubes. The packager bundles the same
+weights into the deployable model archive.
+
+Usage:
+    uv run python scripts/prepare.py \
+        --model-repo pyronear/yolo11s_nimble-narwhal_v6.0.0 \
+        --model-filename best.pt \
+        --output-model-dir data/01_raw/models
+"""
+
+import argparse
+import logging
+from pathlib import Path
+
+from huggingface_hub import hf_hub_download
+
+logging.basicConfig(level=logging.INFO, format="%(levelname)s: %(message)s")
+logger = logging.getLogger(__name__)
+
+
+def main() -> None:
+    parser = argparse.ArgumentParser(description="Download YOLO model.")
+    parser.add_argument(
+        "--model-repo",
+        type=str,
+        required=True,
+        help="HuggingFace model repo ID.",
+    )
+    parser.add_argument(
+        "--model-filename",
+        type=str,
+        required=True,
+        help="Model filename in the HuggingFace repo.",
+    )
+    parser.add_argument(
+        "--output-model-dir",
+        type=Path,
+        required=True,
+        help="Output directory for model weights (data/01_raw/models).",
+    )
+    args = parser.parse_args()
+
+    args.output_model_dir.mkdir(parents=True, exist_ok=True)
+    dst_model = args.output_model_dir / args.model_filename
+    if dst_model.exists():
+        logger.info("Model already exists at %s, skipping.", dst_model)
+    else:
+        logger.info("Downloading %s from %s...", args.model_filename, args.model_repo)
+        hf_hub_download(
+            repo_id=args.model_repo,
+            filename=args.model_filename,
+            local_dir=args.output_model_dir,
+        )
+        logger.info("Model saved to %s", dst_model)
+
+
+if __name__ == "__main__":
+    main()
diff --git a/experiments/temporal-models/tube-multiscale-fusion/scripts/train.py b/experiments/temporal-models/tube-multiscale-fusion/scripts/train.py
new file mode 100644
index 00000000..4027a3b5
--- /dev/null
+++ b/experiments/temporal-models/tube-multiscale-fusion/scripts/train.py
@@ -0,0 +1,157 @@
+"""Train the tube multiscale fusion classifier.
+
+Reads a single named section from ``params.yaml`` (e.g. ``train_dinov2_multiscale``)
+so each DVC stage owns its own params.
+"""
+
+import argparse
+import sys
+from pathlib import Path
+
+import lightning as L
+import torch
+import yaml
+from lightning.pytorch.callbacks import EarlyStopping, ModelCheckpoint
+from lightning.pytorch.loggers import CSVLogger, TensorBoardLogger
+from torch.utils.data import DataLoader
+
+from tube_multiscale_fusion.augment import build_tube_augment
+from tube_multiscale_fusion.dataset import TubePatchDataset
+from tube_multiscale_fusion.lit_module import LitTubeMultiscaleClassifier
+
+
+def main() -> None:
+    parser = argparse.ArgumentParser(description=__doc__)
+    parser.add_argument("--train-dir", type=Path, required=True)
+    parser.add_argument("--val-dir", type=Path, required=True)
+    parser.add_argument("--output-dir", type=Path, required=True)
+    parser.add_argument("--params-path", type=Path, required=True)
+    parser.add_argument("--params-key", required=True, help="Key in params.yaml")
+    args = parser.parse_args()
+
+    full_params = yaml.safe_load(args.params_path.read_text())
+    cfg = full_params[args.params_key]
+    augment_cfg = full_params.get("augment", {"enabled": False})
+    g_cfg = full_params[cfg.get("global_branch_section", "global_branch")]
+    l_cfg = full_params[cfg.get("local_branch_section", "local_branch")]
+    f_cfg = full_params[cfg.get("fusion_section", "fusion")]
+
+    device_name = torch.cuda.get_device_name(0) if torch.cuda.is_available() else "cpu"
+    print(
+        f"CUDA available: {torch.cuda.is_available()} | "
+        f"device count: {torch.cuda.device_count()} | "
+        f"device: {device_name}",
+        file=sys.stderr,
+        flush=True,
+    )
+
+    L.seed_everything(cfg["seed"], workers=True)
+    args.output_dir.mkdir(parents=True, exist_ok=True)
+
+    train_transform = build_tube_augment(augment_cfg, train=True)
+    val_transform = build_tube_augment(augment_cfg, train=False)
+
+    train_ds = TubePatchDataset(
+        args.train_dir,
+        max_frames=cfg["max_frames"],
+        transform=train_transform,
+    )
+    val_ds = TubePatchDataset(
+        args.val_dir,
+        max_frames=cfg["max_frames"],
+        transform=val_transform,
+    )
+
+    train_loader = DataLoader(
+        train_ds,
+        batch_size=cfg["batch_size"],
+        shuffle=True,
+        num_workers=cfg["num_workers"],
+        persistent_workers=cfg["num_workers"] > 0,
+    )
+    val_loader = DataLoader(
+        val_ds,
+        batch_size=cfg["batch_size"],
+        shuffle=False,
+        num_workers=cfg["num_workers"],
+        persistent_workers=cfg["num_workers"] > 0,
+    )
+
+    lit = LitTubeMultiscaleClassifier(
+        backbone=g_cfg["backbone"],
+        max_frames=cfg["max_frames"],
+        global_aggregator_num_layers=g_cfg["aggregator"]["num_layers"],
+        global_aggregator_num_heads=g_cfg["aggregator"]["num_heads"],
+        global_aggregator_ffn_dim=g_cfg["aggregator"]["ffn_dim"],
+        global_aggregator_dropout=g_cfg["aggregator"]["dropout"],
+        grid_size=l_cfg["grid_size"],
+        cell_size=l_cfg["cell_size"],
+        tube_length=l_cfg["tube_length"],
+        temporal_stride=l_cfg["temporal_stride"],
+        local_t_kernel=l_cfg["encoder"]["t_kernel"],
+        local_h_patch=l_cfg["encoder"]["h_patch"],
+        local_w_patch=l_cfg["encoder"]["w_patch"],
+        local_embed_dim=l_cfg["encoder"]["embed_dim"],
+        local_num_layers=l_cfg["encoder"]["num_layers"],
+        local_num_heads=l_cfg["encoder"]["num_heads"],
+        local_ffn_dim=l_cfg["encoder"]["ffn_dim"],
+        local_dropout=l_cfg["encoder"]["dropout"],
+        d_fusion=f_cfg["d_fusion"],
+        fusion_num_layers=f_cfg["num_layers"],
+        fusion_num_heads=f_cfg["num_heads"],
+        fusion_ffn_dim=f_cfg["ffn_dim"],
+        fusion_dropout=f_cfg["dropout"],
+        head_hidden_dim=cfg["head_hidden_dim"],
+        head_dropout=cfg.get("head_dropout", 0.0),
+        learning_rate=cfg["learning_rate"],
+        weight_decay=cfg["weight_decay"],
+        finetune=cfg.get("finetune", False),
+        finetune_last_n_blocks=cfg.get("finetune_last_n_blocks", 0),
+        backbone_lr=cfg.get("backbone_lr"),
+        aggregator_kind=cfg.get("aggregator_kind", "transformer"),
+        encoder_kind=cfg.get("encoder_kind", "tubelet_transformer"),
+        img_size=cfg.get("img_size", 224),
+        pretrained=True,
+        use_cosine_warmup=cfg.get("use_cosine_warmup", False),
+        warmup_frac=cfg.get("warmup_frac", 0.05),
+    )
+
+    callbacks = [
+        ModelCheckpoint(
+            dirpath=args.output_dir,
+            filename="best",
+            monitor="val/f1",
+            mode="max",
+            save_top_k=1,
+            save_weights_only=False,
+        ),
+        EarlyStopping(
+            monitor="val/loss", mode="min", patience=cfg["early_stop_patience"]
+        ),
+    ]
+    loggers = [
+        CSVLogger(save_dir=args.output_dir, name="csv_logs"),
+        TensorBoardLogger(save_dir=args.output_dir, name="tb_logs"),
+    ]
+
+    trainer = L.Trainer(
+        max_epochs=cfg["max_epochs"],
+        callbacks=callbacks,
+        logger=loggers,
+        log_every_n_steps=10,
+        deterministic=True,
+        accelerator="auto",
+        devices=1,
+    )
+    trainer.fit(lit, train_loader, val_loader)
+
+    best = args.output_dir / "best.ckpt"
+    target = args.output_dir / "best_checkpoint.pt"
+    if best.exists():
+        if target.exists():
+            target.unlink()
+        best.rename(target)
+
+
+if __name__ == "__main__":
+    main()
diff --git a/experiments/temporal-models/tube-multiscale-fusion/scripts/train_ablation.py b/experiments/temporal-models/tube-multiscale-fusion/scripts/train_ablation.py
new file mode 100644
index 00000000..d5b3c081
--- /dev/null
+++ b/experiments/temporal-models/tube-multiscale-fusion/scripts/train_ablation.py
@@ -0,0 +1,139 @@
+"""Train the no-temporal-module ablation (full model minus the global branch).
+
+Reuses the same ``global_branch`` (ignored), ``local_branch``, and ``fusion``
+sections as the full model so the kept components are byte-for-byte identical.
+"""
+
+import argparse
+import sys
+from pathlib import Path
+
+import lightning as L
+import torch
+import yaml
+from lightning.pytorch.callbacks import EarlyStopping, ModelCheckpoint
+from lightning.pytorch.loggers import CSVLogger, TensorBoardLogger
+from torch.utils.data import DataLoader
+
+from tube_multiscale_fusion.augment import build_tube_augment
+from tube_multiscale_fusion.dataset import TubePatchDataset
+from tube_multiscale_fusion.lit_ablation import LitAblationNoTemporal
+
+
+def main() -> None:
+    parser = argparse.ArgumentParser(description=__doc__)
+    parser.add_argument("--train-dir", type=Path, required=True)
+    parser.add_argument("--val-dir", type=Path, required=True)
+    parser.add_argument("--output-dir", type=Path, required=True)
+    parser.add_argument("--params-path", type=Path, required=True)
+    parser.add_argument("--params-key", required=True, help="Key in params.yaml")
+    args = parser.parse_args()
+
+    full_params = yaml.safe_load(args.params_path.read_text())
+    cfg = full_params[args.params_key]
+    augment_cfg = full_params.get("augment", {"enabled": False})
+    l_cfg = full_params[cfg.get("local_branch_section", "local_branch")]
+    f_cfg = full_params[cfg.get("fusion_section", "fusion")]
+
+    device_name = torch.cuda.get_device_name(0) if torch.cuda.is_available() else "cpu"
+    print(
+        f"CUDA available: {torch.cuda.is_available()} | device: {device_name}",
+        file=sys.stderr,
+        flush=True,
+    )
+
+    L.seed_everything(cfg["seed"], workers=True)
+    args.output_dir.mkdir(parents=True, exist_ok=True)
+
+    train_transform = build_tube_augment(augment_cfg, train=True)
+    val_transform = build_tube_augment(augment_cfg, train=False)
+
+    train_ds = TubePatchDataset(
+        args.train_dir, max_frames=cfg["max_frames"], transform=train_transform
+    )
+    val_ds = TubePatchDataset(
+        args.val_dir, max_frames=cfg["max_frames"], transform=val_transform
+    )
+
+    train_loader = DataLoader(
+        train_ds,
+        batch_size=cfg["batch_size"],
+        shuffle=True,
+        num_workers=cfg["num_workers"],
+        persistent_workers=cfg["num_workers"] > 0,
+    )
+    val_loader = DataLoader(
+        val_ds,
+        batch_size=cfg["batch_size"],
+        shuffle=False,
+        num_workers=cfg["num_workers"],
+        persistent_workers=cfg["num_workers"] > 0,
+    )
+
+    lit = LitAblationNoTemporal(
+        grid_size=l_cfg["grid_size"],
+        cell_size=l_cfg["cell_size"],
+        tube_length=l_cfg["tube_length"],
+        temporal_stride=l_cfg["temporal_stride"],
+        local_t_kernel=l_cfg["encoder"]["t_kernel"],
+        local_h_patch=l_cfg["encoder"]["h_patch"],
+        local_w_patch=l_cfg["encoder"]["w_patch"],
+        local_embed_dim=l_cfg["encoder"]["embed_dim"],
+        local_num_layers=l_cfg["encoder"]["num_layers"],
+        local_num_heads=l_cfg["encoder"]["num_heads"],
+        local_ffn_dim=l_cfg["encoder"]["ffn_dim"],
+        local_dropout=l_cfg["encoder"]["dropout"],
+        d_fusion=f_cfg["d_fusion"],
+        fusion_num_layers=f_cfg["num_layers"],
+        fusion_num_heads=f_cfg["num_heads"],
+        fusion_ffn_dim=f_cfg["ffn_dim"],
+        fusion_dropout=f_cfg["dropout"],
+        head_hidden_dim=cfg["head_hidden_dim"],
+        head_dropout=cfg.get("head_dropout", 0.0),
+        query_dim=cfg.get("query_dim", 384),
+        encoder_kind=cfg.get("encoder_kind", "tubelet_transformer"),
+        learning_rate=cfg["learning_rate"],
+        weight_decay=cfg["weight_decay"],
+        use_cosine_warmup=cfg.get("use_cosine_warmup", False),
+        warmup_frac=cfg.get("warmup_frac", 0.05),
+    )
+
+    callbacks = [
+        ModelCheckpoint(
+            dirpath=args.output_dir,
+            filename="best",
+            monitor="val/f1",
+            mode="max",
+            save_top_k=1,
+            save_weights_only=False,
+        ),
+        EarlyStopping(
+            monitor="val/f1", mode="max", patience=cfg["early_stop_patience"]
+        ),
+    ]
+    loggers = [
+        CSVLogger(save_dir=args.output_dir, name="csv_logs"),
+        TensorBoardLogger(save_dir=args.output_dir, name="tb_logs"),
+    ]
+
+    trainer = L.Trainer(
+        max_epochs=cfg["max_epochs"],
+        callbacks=callbacks,
+        logger=loggers,
+        log_every_n_steps=10,
+        deterministic=True,
+        accelerator="auto",
+        devices=1,
+    )
+    trainer.fit(lit, train_loader, val_loader)
+
+    best = args.output_dir / "best.ckpt"
+    target = args.output_dir / "best_checkpoint.pt"
+    if best.exists():
+        if target.exists():
+            target.unlink()
+        best.rename(target)
+
+
+if __name__ == "__main__":
+    main()
diff --git a/experiments/temporal-models/tube-multiscale-fusion/scripts/train_ablation_global.py b/experiments/temporal-models/tube-multiscale-fusion/scripts/train_ablation_global.py
new file mode 100644
index 00000000..d1db080e
--- /dev/null
+++ b/experiments/temporal-models/tube-multiscale-fusion/scripts/train_ablation_global.py
@@ -0,0 +1,147 @@
+"""Train a global-branch ablation variant (no_spatial or weighted_mean).
+
+Reads the variant's training block plus the shared ``global_branch`` /
+``local_branch`` / ``fusion`` sections so the retained components match the
+full model byte-for-byte.
+"""
+
+import argparse
+import sys
+from pathlib import Path
+
+import lightning as L
+import torch
+import yaml
+from lightning.pytorch.callbacks import EarlyStopping, ModelCheckpoint
+from lightning.pytorch.loggers import CSVLogger, TensorBoardLogger
+from torch.utils.data import DataLoader
+
+from tube_multiscale_fusion.augment import build_tube_augment
+from tube_multiscale_fusion.dataset import TubePatchDataset
+from tube_multiscale_fusion.lit_ablation import LitAblationGlobal
+
+
+def main() -> None:
+    parser = argparse.ArgumentParser(description=__doc__)
+    parser.add_argument("--train-dir", type=Path, required=True)
+    parser.add_argument("--val-dir", type=Path, required=True)
+    parser.add_argument("--output-dir", type=Path, required=True)
+    parser.add_argument("--params-path", type=Path, required=True)
+    parser.add_argument("--params-key", required=True)
+    args = parser.parse_args()
+
+    full_params = yaml.safe_load(args.params_path.read_text())
+    cfg = full_params[args.params_key]
+    augment_cfg = full_params.get("augment", {"enabled": False})
+    g_cfg = full_params[cfg.get("global_branch_section", "global_branch")]
+    l_cfg = full_params[cfg.get("local_branch_section", "local_branch")]
+    f_cfg = full_params[cfg.get("fusion_section", "fusion")]
+
+    device_name = torch.cuda.get_device_name(0) if torch.cuda.is_available() else "cpu"
+    print(
+        f"CUDA available: {torch.cuda.is_available()} | device: {device_name}",
+        file=sys.stderr,
+        flush=True,
+    )
+
+    L.seed_everything(cfg["seed"], workers=True)
+    args.output_dir.mkdir(parents=True, exist_ok=True)
+
+    train_transform = build_tube_augment(augment_cfg, train=True)
+    val_transform = build_tube_augment(augment_cfg, train=False)
+
+    train_ds = TubePatchDataset(
+        args.train_dir, max_frames=cfg["max_frames"], transform=train_transform
+    )
+    val_ds = TubePatchDataset(
+        args.val_dir, max_frames=cfg["max_frames"], transform=val_transform
+    )
+    train_loader = DataLoader(
+        train_ds,
+        batch_size=cfg["batch_size"],
+        shuffle=True,
+        num_workers=cfg["num_workers"],
+        persistent_workers=cfg["num_workers"] > 0,
+    )
+    val_loader = DataLoader(
+        val_ds,
+        batch_size=cfg["batch_size"],
+        shuffle=False,
+        num_workers=cfg["num_workers"],
+        persistent_workers=cfg["num_workers"] > 0,
+    )
+
+    lit = LitAblationGlobal(
+        variant=cfg["ablation_variant"],
+        backbone=g_cfg["backbone"],
+        max_frames=cfg["max_frames"],
+        global_aggregator_num_layers=g_cfg["aggregator"]["num_layers"],
+        global_aggregator_num_heads=g_cfg["aggregator"]["num_heads"],
+        global_aggregator_ffn_dim=g_cfg["aggregator"]["ffn_dim"],
+        global_aggregator_dropout=g_cfg["aggregator"]["dropout"],
+        grid_size=l_cfg["grid_size"],
+        cell_size=l_cfg["cell_size"],
+        tube_length=l_cfg["tube_length"],
+        temporal_stride=l_cfg["temporal_stride"],
+        local_t_kernel=l_cfg["encoder"]["t_kernel"],
+        local_h_patch=l_cfg["encoder"]["h_patch"],
+        local_w_patch=l_cfg["encoder"]["w_patch"],
+        local_embed_dim=l_cfg["encoder"]["embed_dim"],
+        local_num_layers=l_cfg["encoder"]["num_layers"],
+        local_num_heads=l_cfg["encoder"]["num_heads"],
+        local_ffn_dim=l_cfg["encoder"]["ffn_dim"],
+        local_dropout=l_cfg["encoder"]["dropout"],
+        d_fusion=f_cfg["d_fusion"],
+        head_hidden_dim=cfg["head_hidden_dim"],
+        head_dropout=cfg.get("head_dropout", 0.0),
+        learning_rate=cfg["learning_rate"],
+        weight_decay=cfg["weight_decay"],
+        finetune=cfg.get("finetune", False),
+        finetune_last_n_blocks=cfg.get("finetune_last_n_blocks", 0),
+        backbone_lr=cfg.get("backbone_lr"),
+        aggregator_kind=cfg.get("aggregator_kind", "transformer"),
+        img_size=cfg.get("img_size", 224),
+        pretrained=True,
+        use_cosine_warmup=cfg.get("use_cosine_warmup", False),
+        warmup_frac=cfg.get("warmup_frac", 0.05),
+    )
+
+    callbacks = [
+        ModelCheckpoint(
+            dirpath=args.output_dir,
+            filename="best",
+            monitor="val/f1",
+            mode="max",
+            save_top_k=1,
+            save_weights_only=False,
+        ),
+        EarlyStopping(
+            monitor="val/f1", mode="max", patience=cfg["early_stop_patience"]
+        ),
+    ]
+    loggers = [
+        CSVLogger(save_dir=args.output_dir, name="csv_logs"),
+        TensorBoardLogger(save_dir=args.output_dir, name="tb_logs"),
+    ]
+
+    trainer = L.Trainer(
+        max_epochs=cfg["max_epochs"],
+        callbacks=callbacks,
+        logger=loggers,
+        log_every_n_steps=10,
+        deterministic=True,
+        accelerator="auto",
+        devices=1,
+    )
+    trainer.fit(lit, train_loader, val_loader)
+
+    best = args.output_dir / "best.ckpt"
+    target = args.output_dir / "best_checkpoint.pt"
+    if best.exists():
+        if target.exists():
+            target.unlink()
+        best.rename(target)
+
+
+if __name__ == "__main__":
+    main()
diff --git a/experiments/temporal-models/tube-multiscale-fusion/scripts/truncate_sequences.py b/experiments/temporal-models/tube-multiscale-fusion/scripts/truncate_sequences.py
new file mode 100644
index 00000000..c083d34b
--- /dev/null
+++ b/experiments/temporal-models/tube-multiscale-fusion/scripts/truncate_sequences.py
@@ -0,0 +1,82 @@
+"""Truncate sequences to a maximum number of frames.
+
+Copies sequences from an input directory to an output directory,
+keeping only the first N frames (sorted by filename/timestamp).
+Preserves the {wildfire,fp}/<sequence>/{images,labels}/ structure.
+"""
+
+import argparse
+import shutil
+from pathlib import Path
+
+
+def parse_args() -> argparse.Namespace:
+    parser = argparse.ArgumentParser(description=__doc__)
+    parser.add_argument(
+        "--input-dir",
+        type=Path,
+        required=True,
+        help="Input split directory (e.g. data/01_raw/datasets_full/train)",
+    )
+    parser.add_argument(
+        "--output-dir",
+        type=Path,
+        required=True,
+        help="Output split directory (e.g. data/01_raw/datasets/train)",
+    )
+    parser.add_argument(
+        "--max-frames",
+        type=int,
+        default=20,
+        help="Maximum number of frames per sequence (default: 20)",
+    )
+    return parser.parse_args()
+
+
+def truncate_sequence(src_seq: Path, dst_seq: Path, max_frames: int) -> None:
+    """Copy a sequence directory, truncating to max_frames."""
+    src_images = sorted((src_seq / "images").glob("*.jpg"))
+    src_labels = src_seq / "labels"
+
+    # Keep first max_frames (earliest by filename/timestamp)
+    kept_images = src_images[:max_frames]
+    kept_stems = {p.stem for p in kept_images}
+
+    # Copy images
+    dst_images = dst_seq / "images"
+    dst_images.mkdir(parents=True, exist_ok=True)
+    for img in kept_images:
+        shutil.copy2(img, dst_images / img.name)
+
+    # Copy matching labels
+    dst_labels = dst_seq / "labels"
+    dst_labels.mkdir(parents=True, exist_ok=True)
+    if src_labels.is_dir():
+        for label_file in sorted(src_labels.glob("*.txt")):
+            if label_file.stem in kept_stems:
+                shutil.copy2(label_file, dst_labels / label_file.name)
+
+
+def main() -> None:
+    args = parse_args()
+
+    for category in ("wildfire", "fp"):
+        src_cat = args.input_dir / category
+        if not src_cat.is_dir():
+            print(f"Skipping {category}: {src_cat} not found")
+            continue
+
+        sequences = sorted(d for d in src_cat.iterdir() if d.is_dir())
+        print(f"{category}: {len(sequences)} sequences")
+
+        for seq_dir in sequences:
+            dst_seq = args.output_dir / category / seq_dir.name
+            if dst_seq.exists():
+                continue
+            truncate_sequence(seq_dir, dst_seq, args.max_frames)
+
+    print("Done.")
+
+
+if __name__ == "__main__":
+    main()
diff --git a/experiments/temporal-models/tube-multiscale-fusion/src/tube_multiscale_fusion/__init__.py b/experiments/temporal-models/tube-multiscale-fusion/src/tube_multiscale_fusion/__init__.py
new file mode 100644
index 00000000..f1d41c51
--- /dev/null
+++ b/experiments/temporal-models/tube-multiscale-fusion/src/tube_multiscale_fusion/__init__.py
@@ -0,0 +1,20 @@
+"""Tube multiscale fusion: global DINOv2 context + local tube transformer."""
+
+from .classifier import TubeMultiscaleClassifier
+from .fusion import FusionModule
+from .global_branch import GlobalBranch, TimmBackbone
+from .lit_module import LitTubeMultiscaleClassifier
+from .local_branch import LocalBranch, LocalTubeEncoder, extract_tubes
+from .model import TubeMultiscaleFusionModel
+
+__all__ = [
+    "FusionModule",
+    "GlobalBranch",
+    "LitTubeMultiscaleClassifier",
+    "LocalBranch",
+    "LocalTubeEncoder",
+    "TimmBackbone",
+    "TubeMultiscaleClassifier",
+    "TubeMultiscaleFusionModel",
+    "extract_tubes",
+]
diff --git a/experiments/temporal-models/tube-multiscale-fusion/src/tube_multiscale_fusion/ablation_classifier.py b/experiments/temporal-models/tube-multiscale-fusion/src/tube_multiscale_fusion/ablation_classifier.py
new file mode 100644
index 00000000..07e4d4ab
--- /dev/null
+++ b/experiments/temporal-models/tube-multiscale-fusion/src/tube_multiscale_fusion/ablation_classifier.py
@@ -0,0 +1,238 @@
+"""Ablation: the full model with the temporal (global) module removed.
+
+This is a *faithful* ablation of :class:`TubeMultiscaleClassifier`: the local
+branch and the fusion module are the **exact same modules** with the **same
+hyperparameters**; the only thing removed is the global DINOv2 sequence branch
+(the "temporal module" that embeds all 16 frames and aggregates them into a
+context vector).
+
+Because the fusion module expects a query vector (in the full model: the global
+context vector), we replace that single input with a **learnable query token**
+— a constant, data-independent parameter. Everything downstream (self-attention
+over local tubes, cross-attention query→locals, FFN, MLP head) is identical.
+
+Removing the global branch removes the 16 per-frame DINOv2 forward passes, which
+dominate the full model's compute — so this ablation has strictly *fewer*
+parameters and FLOPs than the full model, isolating the contribution of the
+global temporal context.
+"""
+
+from __future__ import annotations
+
+import torch
+from torch import Tensor, nn
+
+from .fusion import FusionModule, WeightedMeanFusion
+from .global_branch import GlobalBranch
+from .local_branch import LocalBranch
+
+
+class AblationNoTemporalClassifier(nn.Module):
+    """Full model minus the global/temporal branch (learnable query in its place)."""
+
+    def __init__(
+        self,
+        # Local branch — identical to the full model
+        grid_size: int,
+        cell_size: int,
+        tube_length: int,
+        temporal_stride: int,
+        local_t_kernel: int,
+        local_h_patch: int,
+        local_w_patch: int,
+        local_embed_dim: int,
+        local_num_layers: int,
+        local_num_heads: int,
+        local_ffn_dim: int,
+        local_dropout: float,
+        # Fusion — identical to the full model
+        d_fusion: int,
+        fusion_num_layers: int,
+        fusion_num_heads: int,
+        fusion_ffn_dim: int,
+        fusion_dropout: float,
+        # Head — identical to the full model
+        head_hidden_dim: int,
+        head_dropout: float = 0.0,
+        # Dim of the learnable query that stands in for the global vector.
+        query_dim: int = 384,
+        # Per-tube spatial encoder (the module under test in the spatial study).
+        encoder_kind: str = "tubelet_transformer",
+    ) -> None:
+        super().__init__()
+        self.local_branch = LocalBranch(
+            grid_size=grid_size,
+            cell_size=cell_size,
+            tube_length=tube_length,
+            temporal_stride=temporal_stride,
+            t_kernel=local_t_kernel,
+            h_patch=local_h_patch,
+            w_patch=local_w_patch,
+            embed_dim=local_embed_dim,
+            num_layers=local_num_layers,
+            num_heads=local_num_heads,
+            ffn_dim=local_ffn_dim,
+            dropout=local_dropout,
+            encoder_kind=encoder_kind,
+        )
+        # Learnable stand-in for the (removed) global context vector.
+        self.query_token = nn.Parameter(torch.zeros(1, query_dim))
+        nn.init.trunc_normal_(self.query_token, std=0.02)
+        self.fusion = FusionModule(
+            global_dim=query_dim,
+            local_dim=local_embed_dim,
+            d_fusion=d_fusion,
+            num_layers=fusion_num_layers,
+            num_heads=fusion_num_heads,
+            ffn_dim=fusion_ffn_dim,
+            dropout=fusion_dropout,
+        )
+        self.head = nn.Sequential(
+            nn.Linear(d_fusion, head_hidden_dim),
+            nn.GELU(),
+            nn.Dropout(head_dropout),
+            nn.Linear(head_hidden_dim, 1),
+        )
+
+    def forward(self, patches: Tensor, mask: Tensor) -> Tensor:
+        local_tokens, local_mask = self.local_branch(patches, mask)
+        query = self.query_token.expand(patches.shape[0], -1)
+        fused = self.fusion(query, local_tokens, local_mask)
+        return self.head(fused).squeeze(-1)
+
+
+class AblationNoSpatialClassifier(nn.Module):
+    """Full model minus the spatial (local tube) branch — global branch only.
+
+    Keeps the global DINOv2 sequence branch exactly as in the full model and
+    classifies its context vector directly with the same MLP head. No local
+    branch, no fusion. Isolates the standalone power of the global branch.
+    """
+
+    def __init__(
+        self,
+        backbone: str,
+        finetune: bool,
+        finetune_last_n_blocks: int,
+        max_frames: int,
+        global_aggregator_num_layers: int,
+        global_aggregator_num_heads: int,
+        global_aggregator_ffn_dim: int,
+        global_aggregator_dropout: float,
+        head_hidden_dim: int,
+        head_dropout: float = 0.0,
+        aggregator_kind: str = "transformer",
+        img_size: int = 224,
+        pretrained: bool = True,
+    ) -> None:
+        super().__init__()
+        self.global_branch = GlobalBranch(
+            backbone=backbone,
+            finetune=finetune,
+            finetune_last_n_blocks=finetune_last_n_blocks,
+            max_frames=max_frames,
+            aggregator_num_layers=global_aggregator_num_layers,
+            aggregator_num_heads=global_aggregator_num_heads,
+            aggregator_ffn_dim=global_aggregator_ffn_dim,
+            aggregator_dropout=global_aggregator_dropout,
+            aggregator_kind=aggregator_kind,
+            img_size=img_size,
+            pretrained=pretrained,
+        )
+        self.head = nn.Sequential(
+            nn.Linear(self.global_branch.feat_dim, head_hidden_dim),
+            nn.GELU(),
+            nn.Dropout(head_dropout),
+            nn.Linear(head_hidden_dim, 1),
+        )
+
+    def forward(self, patches: Tensor, mask: Tensor) -> Tensor:
+        global_vec = self.global_branch(patches, mask)
+        return self.head(global_vec).squeeze(-1)
+
+
+class AblationWeightedMeanClassifier(nn.Module):
+    """Full model with cross-attention fusion replaced by weighted means.
+
+    Both branches are kept exactly as in the full model; only the fusion module
+    is swapped from :class:`FusionModule` (self-attn over tubes + global→locals
+    cross-attention) to :class:`WeightedMeanFusion` (learned weighted mean over
+    tubes + learned gate between the two branches). Isolates the contribution of
+    the attention mechanism in the fusion step.
+    """
+
+    def __init__(
+        self,
+        # Global branch — identical to the full model
+        backbone: str,
+        finetune: bool,
+        finetune_last_n_blocks: int,
+        max_frames: int,
+        global_aggregator_num_layers: int,
+        global_aggregator_num_heads: int,
+        global_aggregator_ffn_dim: int,
+        global_aggregator_dropout: float,
+        # Local branch — identical to the full model
+        grid_size: int,
+        cell_size: int,
+        tube_length: int,
+        temporal_stride: int,
+        local_t_kernel: int,
+        local_h_patch: int,
+        local_w_patch: int,
+        local_embed_dim: int,
+        local_num_layers: int,
+        local_num_heads: int,
+        local_ffn_dim: int,
+        local_dropout: float,
+        # Fusion / head
+        d_fusion: int,
+        head_hidden_dim: int,
+        head_dropout: float = 0.0,
+        img_size: int = 224,
+        pretrained: bool = True,
+    ) -> None:
+        super().__init__()
+        self.global_branch = GlobalBranch(
+            backbone=backbone,
+            finetune=finetune,
+            finetune_last_n_blocks=finetune_last_n_blocks,
+            max_frames=max_frames,
+            aggregator_num_layers=global_aggregator_num_layers,
+            aggregator_num_heads=global_aggregator_num_heads,
+            aggregator_ffn_dim=global_aggregator_ffn_dim,
+            aggregator_dropout=global_aggregator_dropout,
+            img_size=img_size,
+            pretrained=pretrained,
+        )
+        self.local_branch = LocalBranch(
+            grid_size=grid_size,
+            cell_size=cell_size,
+            tube_length=tube_length,
+            temporal_stride=temporal_stride,
+            t_kernel=local_t_kernel,
+            h_patch=local_h_patch,
+            w_patch=local_w_patch,
+            embed_dim=local_embed_dim,
+            num_layers=local_num_layers,
+            num_heads=local_num_heads,
+            ffn_dim=local_ffn_dim,
+            dropout=local_dropout,
+        )
+        self.fusion = WeightedMeanFusion(
+            global_dim=self.global_branch.feat_dim,
+            local_dim=local_embed_dim,
+            d_fusion=d_fusion,
+        )
+        self.head = nn.Sequential(
+            nn.Linear(d_fusion, head_hidden_dim),
+            nn.GELU(),
+            nn.Dropout(head_dropout),
+            nn.Linear(head_hidden_dim, 1),
+        )
+
+    def forward(self, patches: Tensor, mask: Tensor) -> Tensor:
+        global_vec = self.global_branch(patches, mask)
+        local_tokens, local_mask = self.local_branch(patches, mask)
+        fused = self.fusion(global_vec, local_tokens, local_mask)
+        return self.head(fused).squeeze(-1)
diff --git a/experiments/temporal-models/tube-multiscale-fusion/src/tube_multiscale_fusion/augment.py b/experiments/temporal-models/tube-multiscale-fusion/src/tube_multiscale_fusion/augment.py
new file mode 100644
index 00000000..af058304
--- /dev/null
+++ b/experiments/temporal-models/tube-multiscale-fusion/src/tube_multiscale_fusion/augment.py
@@ -0,0 +1,256 @@
+"""Per-tube-consistent augmentation transforms for the temporal classifier.
+
+See ``docs/specs/2026-04-14-training-augmentation-design.md`` for design.
+"""
+
+from __future__ import annotations
+
+from collections.abc import Callable
+
+import torch
+from torch import Tensor
+from torchvision.transforms.v2 import functional as TVF
+
+
+class SpatialTubeTransform:
+    """Per-tube-consistent spatial aug (flip + rotation + scale + translate).
+
+    A single set of params is sampled per call and applied identically to
+    every frame in the tube so motion direction and inter-frame geometry
+    are preserved.
+    """
+
+    def __init__(
+        self,
+        flip_prob: float,
+        rotation_deg: float,
+        scale_range: tuple[float, float],
+        translate_frac: float,
+    ) -> None:
+        self.flip_prob = flip_prob
+        self.rotation_deg = rotation_deg
+        self.scale_range = scale_range
+        self.translate_frac = translate_frac
+
+    def __call__(self, item: dict) -> dict:
+        patches: Tensor = item["patches"]  # [T, 3, H, W]
+        _, _, h, w = patches.shape
+
+        # Sample once per tube
+        do_flip = torch.rand(()).item() < self.flip_prob
+        angle = float((torch.rand(()).item() * 2 - 1) * self.rotation_deg)
+        s_lo, s_hi = self.scale_range
+        scale = float(s_lo + torch.rand(()).item() * (s_hi - s_lo))
+        tx = float((torch.rand(()).item() * 2 - 1) * self.translate_frac * w)
+        ty = float((torch.rand(()).item() * 2 - 1) * self.translate_frac * h)
+
+        if do_flip:
+            patches = TVF.horizontal_flip(patches)
+
+        # Skip affine call entirely when parameters are no-ops (identity).
+        is_identity_affine = angle == 0.0 and scale == 1.0 and tx == 0.0 and ty == 0.0
+        if not is_identity_affine:
+            patches = TVF.affine(
+                patches,
+                angle=angle,
+                translate=[tx, ty],
+                scale=scale,
+                shear=[0.0, 0.0],
+            )
+
+        item["patches"] = patches
+        return item
+
+
+class PhotometricTubeTransform:
+    """Per-tube-consistent brightness / contrast / saturation.
+
+    Operates on ``[0, 1]`` tensors (pre-normalization). One set of factors
+    sampled per call, applied in fixed order (brightness -> contrast ->
+    saturation) to every frame.
+    """
+
+    def __init__(
+        self,
+        brightness_range: tuple[float, float],
+        contrast_range: tuple[float, float],
+        saturation_range: tuple[float, float],
+    ) -> None:
+        self.brightness_range = brightness_range
+        self.contrast_range = contrast_range
+        self.saturation_range = saturation_range
+
+    @staticmethod
+    def _sample(r: tuple[float, float]) -> float:
+        lo, hi = r
+        return float(lo + torch.rand(()).item() * (hi - lo))
+
+    def __call__(self, item: dict) -> dict:
+        patches: Tensor = item["patches"]  # [T, 3, H, W], values in [0, 1]
+
+        b = self._sample(self.brightness_range)
+        c = self._sample(self.contrast_range)
+        s = self._sample(self.saturation_range)
+
+        if b != 1.0:
+            patches = TVF.adjust_brightness(patches, brightness_factor=b)
+        if c != 1.0:
+            patches = TVF.adjust_contrast(patches, contrast_factor=c)
+        if s != 1.0:
+            patches = TVF.adjust_saturation(patches, saturation_factor=s)
+
+        # Clamp to valid photometric range; adjust_contrast can push slightly
+        # below 0 / above 1 and the downstream ImageNet normalize expects [0, 1].
+        item["patches"] = patches.clamp_(0.0, 1.0)
+        return item
+
+
+class TemporalTubeTransform:
+    """Sub-sequence sampling + random stride + per-frame drop with re-compaction.
+
+    Operates on ``(patches: [T, 3, H, W], mask: [T])``. Output valid frames
+    always occupy positions ``[0..k-1]`` so ``pack_padded_sequence`` (used by
+    the GRU head) sees a contiguous valid prefix.
+    """
+
+    def __init__(
+        self,
+        subseq_prob: float,
+        subseq_min_len: int,
+        stride_prob: float,
+        frame_drop_prob: float,
+        min_valid_after_drop: int,
+    ) -> None:
+        self.subseq_prob = subseq_prob
+        self.subseq_min_len = subseq_min_len
+        self.stride_prob = stride_prob
+        self.frame_drop_prob = frame_drop_prob
+        self.min_valid_after_drop = min_valid_after_drop
+
+    def __call__(self, item: dict) -> dict:
+        patches: Tensor = item["patches"]  # [T, 3, H, W]
+        mask: Tensor = item["mask"]  # [T] bool
+
+        valid_idx = torch.nonzero(mask, as_tuple=False).flatten().tolist()
+        n = len(valid_idx)
+        if n == 0:
+            return item
+
+        # 1. Sub-sequence sampling
+        if torch.rand(()).item() < self.subseq_prob and n > self.subseq_min_len:
+            k = int(torch.randint(self.subseq_min_len, n + 1, (1,)).item())
+            start = int(torch.randint(0, n - k + 1, (1,)).item())
+            valid_idx = valid_idx[start : start + k]
+
+        # 2. Random stride
+        if torch.rand(()).item() < self.stride_prob and len(valid_idx) > 2:
+            valid_idx = valid_idx[::2]
+
+        # 3. Per-frame drop, clamped to a floor
+        if self.frame_drop_prob > 0.0 and len(valid_idx) > self.min_valid_after_drop:
+            keeps = torch.rand(len(valid_idx)) >= self.frame_drop_prob
+            # Enforce floor: if we dropped too many, randomly restore indices.
+            n_kept = int(keeps.sum())
+            if n_kept < self.min_valid_after_drop:
+                dropped_positions = torch.nonzero(~keeps, as_tuple=False).flatten()
+                n_to_restore = self.min_valid_after_drop - n_kept
+                perm = torch.randperm(len(dropped_positions))[:n_to_restore]
+                for pos in dropped_positions[perm].tolist():
+                    keeps[pos] = True
+            valid_idx = [
+                idx
+                for idx, keep in zip(valid_idx, keeps.tolist(), strict=False)
+                if keep
+            ]
+
+        # Re-compact to a fresh padded tensor via one vectorized gather.
+        out_patches = torch.zeros_like(patches)
+        out_mask = torch.zeros(mask.shape, dtype=torch.bool, device=mask.device)
+        k = len(valid_idx)
+        if k > 0:
+            idx = torch.as_tensor(valid_idx, dtype=torch.long, device=patches.device)
+            out_patches[:k] = patches.index_select(0, idx)
+            out_mask[:k] = True
+
+        item["patches"] = out_patches
+        item["mask"] = out_mask
+        return item
+
+
+IMAGENET_MEAN = (0.485, 0.456, 0.406)
+IMAGENET_STD = (0.229, 0.224, 0.225)
+
+
+class NormalizeTransform:
+    """ImageNet normalize of the valid frames in a tube (mask-aware).
+
+    Writes to a fresh output tensor so the caller's input is never mutated.
+    mean/std are lazily moved to the input tensor's device and dtype.
+    """
+
+    def __init__(
+        self,
+        mean: tuple[float, float, float] = IMAGENET_MEAN,
+        std: tuple[float, float, float] = IMAGENET_STD,
+    ) -> None:
+        self.mean = torch.tensor(mean).view(3, 1, 1)
+        self.std = torch.tensor(std).view(3, 1, 1)
+
+    def __call__(self, item: dict) -> dict:
+        patches: Tensor = item["patches"]
+        mask: Tensor = item["mask"]
+        n = int(mask.sum())
+        if n > 0:
+            mean = self.mean.to(patches.device, patches.dtype)
+            std = self.std.to(patches.device, patches.dtype)
+            out = patches.clone()
+            out[:n] = (patches[:n] - mean) / std
+            item["patches"] = out
+        return item
+
+
+class ComposeTransform:
+    """Tiny fixed-order transform composer. Each stage takes and returns a dict."""
+
+    def __init__(self, stages: list[Callable[[dict], dict]]) -> None:
+        self.stages = stages
+
+    def __call__(self, item: dict) -> dict:
+        for stage in self.stages:
+            item = stage(item)
+        return item
+
+
+def build_tube_augment(config: dict, train: bool) -> ComposeTransform:
+    """Build the transform pipeline from the ``augment:`` config section.
+
+    - ``train=False`` or ``config["enabled"]=False`` -> normalize only.
+    - Otherwise -> spatial -> photometric -> temporal -> normalize.
+
+    A missing ``enabled`` key defaults to ``False`` (disabled). This matches
+    the defensive default in ``scripts/train.py`` when the top-level
+    ``augment:`` section is absent from ``params.yaml``.
+    """
+    enabled = bool(config.get("enabled", False))
+    if not (train and enabled):
+        return ComposeTransform([NormalizeTransform()])
+
+    spatial = SpatialTubeTransform(
+        flip_prob=float(config["spatial"]["flip_prob"]),
+        rotation_deg=float(config["spatial"]["rotation_deg"]),
+        scale_range=tuple(config["spatial"]["scale_range"]),
+        translate_frac=float(config["spatial"]["translate_frac"]),
+    )
+    photometric = PhotometricTubeTransform(
+        brightness_range=tuple(config["photometric"]["brightness_range"]),
+        contrast_range=tuple(config["photometric"]["contrast_range"]),
+        saturation_range=tuple(config["photometric"]["saturation_range"]),
+    )
+    temporal = TemporalTubeTransform(
+        subseq_prob=float(config["temporal"]["subseq_prob"]),
+        subseq_min_len=int(config["temporal"]["subseq_min_len"]),
+        stride_prob=float(config["temporal"]["stride_prob"]),
+        frame_drop_prob=float(config["temporal"]["frame_drop_prob"]),
+        min_valid_after_drop=int(config["temporal"]["min_valid_after_drop"]),
+    )
+    return ComposeTransform([spatial, photometric, temporal, NormalizeTransform()])
diff --git a/experiments/temporal-models/tube-multiscale-fusion/src/tube_multiscale_fusion/classifier.py b/experiments/temporal-models/tube-multiscale-fusion/src/tube_multiscale_fusion/classifier.py
new file mode 100644
index 00000000..89604340
--- /dev/null
+++ b/experiments/temporal-models/tube-multiscale-fusion/src/tube_multiscale_fusion/classifier.py
@@ -0,0 +1,111 @@
+"""Top-level tube multiscale fusion classifier.
+
+Combines:
+    - :class:`GlobalBranch`: per-frame DINOv2 + transformer aggregator -> ``(B, D_g)``
+    - :class:`LocalBranch`: spatio-temporal tube extraction + video transformer
+      -> ``(B, N_tubes, D_l)`` + validity mask ``(B, N_tubes)``
+    - :class:`FusionModule`: self-attn on locals + cross-attn (global=Q, locals=KV)
+      -> ``(B, d_fusion)``
+    - 2-layer MLP head -> logit ``(B,)``.
+"""
+
+from __future__ import annotations
+
+from torch import Tensor, nn
+
+from .fusion import FusionModule
+from .global_branch import GlobalBranch
+from .local_branch import LocalBranch
+
+
+class TubeMultiscaleClassifier(nn.Module):
+    def __init__(
+        self,
+        # Global branch
+        backbone: str,
+        finetune: bool,
+        finetune_last_n_blocks: int,
+        max_frames: int,
+        global_aggregator_num_layers: int,
+        global_aggregator_num_heads: int,
+        global_aggregator_ffn_dim: int,
+        global_aggregator_dropout: float,
+        # Local branch
+        grid_size: int,
+        cell_size: int,
+        tube_length: int,
+        temporal_stride: int,
+        local_t_kernel: int,
+        local_h_patch: int,
+        local_w_patch: int,
+        local_embed_dim: int,
+        local_num_layers: int,
+        local_num_heads: int,
+        local_ffn_dim: int,
+        local_dropout: float,
+        # Fusion
+        d_fusion: int,
+        fusion_num_layers: int,
+        fusion_num_heads: int,
+        fusion_ffn_dim: int,
+        fusion_dropout: float,
+        # Head
+        head_hidden_dim: int,
+        head_dropout: float = 0.0,
+        # Module variants (default = production architecture)
+        aggregator_kind: str = "transformer",
+        encoder_kind: str = "tubelet_transformer",
+        # Misc
+        img_size: int = 224,
+        pretrained: bool = True,
+    ) -> None:
+        super().__init__()
+        self.global_branch = GlobalBranch(
+            backbone=backbone,
+            finetune=finetune,
+            finetune_last_n_blocks=finetune_last_n_blocks,
+            max_frames=max_frames,
+            aggregator_num_layers=global_aggregator_num_layers,
+            aggregator_num_heads=global_aggregator_num_heads,
+            aggregator_ffn_dim=global_aggregator_ffn_dim,
+            aggregator_dropout=global_aggregator_dropout,
+            aggregator_kind=aggregator_kind,
+            img_size=img_size,
+            pretrained=pretrained,
+        )
+        self.local_branch = LocalBranch(
+            grid_size=grid_size,
+            cell_size=cell_size,
+            tube_length=tube_length,
+            temporal_stride=temporal_stride,
+            t_kernel=local_t_kernel,
+            h_patch=local_h_patch,
+            w_patch=local_w_patch,
+            embed_dim=local_embed_dim,
+            num_layers=local_num_layers,
+            num_heads=local_num_heads,
+            ffn_dim=local_ffn_dim,
+            dropout=local_dropout,
+            encoder_kind=encoder_kind,
+        )
+        self.fusion = FusionModule(
+            global_dim=self.global_branch.feat_dim,
+            local_dim=local_embed_dim,
+            d_fusion=d_fusion,
+            num_layers=fusion_num_layers,
+            num_heads=fusion_num_heads,
+            ffn_dim=fusion_ffn_dim,
+            dropout=fusion_dropout,
+        )
+        self.head = nn.Sequential(
+            nn.Linear(d_fusion, head_hidden_dim),
+            nn.GELU(),
+            nn.Dropout(head_dropout),
+            nn.Linear(head_hidden_dim, 1),
+        )
+
+    def forward(self, patches: Tensor, mask: Tensor) -> Tensor:
+        global_vec = self.global_branch(patches, mask)
+        local_tokens, local_mask = self.local_branch(patches, mask)
+        fused = self.fusion(global_vec, local_tokens, local_mask)
+        return self.head(fused).squeeze(-1)
diff --git a/experiments/temporal-models/tube-multiscale-fusion/src/tube_multiscale_fusion/dataset.py b/experiments/temporal-models/tube-multiscale-fusion/src/tube_multiscale_fusion/dataset.py
new file mode 100644
index 00000000..69f78f9d
--- /dev/null
+++ b/experiments/temporal-models/tube-multiscale-fusion/src/tube_multiscale_fusion/dataset.py
@@ -0,0 +1,92 @@
+"""PyTorch Dataset for the tube multiscale fusion classifier.
+
+Reads cropped PNG patches produced by ``scripts/build_model_input.py`` and
+returns per-tube tensors padded to a fixed length with a boolean mask.
+"""
+
+from __future__ import annotations
+
+import json
+from collections.abc import Callable
+from pathlib import Path
+
+import torch
+from PIL import Image
+from torch import Tensor
+from torch.utils.data import Dataset
+from torchvision.transforms.functional import to_tensor
+
+IMAGENET_MEAN = torch.tensor([0.485, 0.456, 0.406]).view(3, 1, 1)
+IMAGENET_STD = torch.tensor([0.229, 0.224, 0.225]).view(3, 1, 1)
+
+
+class TubePatchDataset(Dataset):
+    """Dataset of cropped tube patches stored as PNG folders.
+
+    Each item:
+
+    .. code-block:: python
+
+        {
+            "patches": Tensor[max_frames, 3, 224, 224],  # float32
+            "mask":    Tensor[max_frames] bool,           # True = real frame
+            "label":   Tensor[] float32,                  # 0.0 fp, 1.0 smoke
+            "sequence_id": str,
+        }
+
+    When ``transform`` is ``None``, patches are ImageNet-normalized in place.
+    Otherwise patches reach ``transform`` as un-normalized ``[0, 1]`` tensors
+    and the transform is responsible for normalization as its final step.
+
+    Args:
+        split_dir: Directory containing ``_index.json`` and one
+            sub-directory per tube.
+        max_frames: Pad/truncate length.
+        transform: Optional callable ``item -> item`` applied after loading.
+    """
+
+    def __init__(
+        self,
+        split_dir: Path,
+        max_frames: int,
+        transform: Callable[[dict], dict] | None = None,
+    ) -> None:
+        self.split_dir = Path(split_dir)
+        self.max_frames = max_frames
+        self.transform = transform
+        self.index: list[dict] = json.loads(
+            (self.split_dir / "_index.json").read_text()
+        )
+
+    def __len__(self) -> int:
+        return len(self.index)
+
+    def __getitem__(self, idx: int) -> dict[str, Tensor | str]:
+        record = self.index[idx]
+        seq_id: str = record["sequence_id"]
+        label_int: int = record["label_int"]
+        seq_dir = self.split_dir / seq_id
+
+        meta = json.loads((seq_dir / "meta.json").read_text())
+        frame_files = [seq_dir / f["filename"] for f in meta["frames"]]
+        n = min(len(frame_files), self.max_frames)
+
+        patches = torch.zeros(self.max_frames, 3, 224, 224, dtype=torch.float32)
+        mask = torch.zeros(self.max_frames, dtype=torch.bool)
+        for i in range(n):
+            img = Image.open(frame_files[i]).convert("RGB")
+            patches[i] = to_tensor(img)
+            mask[i] = True
+
+        item: dict = {
+            "patches": patches,
+            "mask": mask,
+            "label": torch.tensor(float(label_int), dtype=torch.float32),
+            "sequence_id": seq_id,
+        }
+
+        if self.transform is None:
+            item["patches"][:n] = (item["patches"][:n] - IMAGENET_MEAN) / IMAGENET_STD
+            return item
+
+        return self.transform(item)
diff --git a/experiments/temporal-models/tube-multiscale-fusion/src/tube_multiscale_fusion/eval_plots.py b/experiments/temporal-models/tube-multiscale-fusion/src/tube_multiscale_fusion/eval_plots.py
new file mode 100644
index 00000000..f6610f29
--- /dev/null
+++ b/experiments/temporal-models/tube-multiscale-fusion/src/tube_multiscale_fusion/eval_plots.py
@@ -0,0 +1,105 @@
+"""Shared matplotlib plot helpers for classification eval output."""
+
+from pathlib import Path
+
+import matplotlib.pyplot as plt
+import numpy as np
+from sklearn.metrics import (
+    average_precision_score,
+    precision_recall_curve,
+    roc_auc_score,
+    roc_curve,
+)
+
+
+def plot_confusion_matrix(
+    matrix: np.ndarray,
+    output_path: Path,
+    title: str,
+    normalized: bool,
+) -> None:
+    """Render a 2x2 confusion matrix to ``output_path``.
+
+    Args:
+        matrix: 2x2 array. Rows = actual (fp, smoke). Cols = predicted.
+        output_path: PNG path to write.
+        title: Figure title.
+        normalized: If True, format cells as percentages; else as integers.
+    """
+    labels = ["fp", "smoke"]
+    fig, ax = plt.subplots(figsize=(4, 4))
+    im = ax.imshow(matrix, cmap="Blues")
+    fig.colorbar(im, ax=ax)
+    ax.set_xticks(range(len(labels)))
+    ax.set_yticks(range(len(labels)))
+    ax.set_xticklabels(labels)
+    ax.set_yticklabels(labels)
+    ax.set_xlabel("predicted")
+    ax.set_ylabel("actual")
+    ax.set_title(title)
+    vmax = float(matrix.max()) if matrix.size else 0.0
+    threshold = vmax * 0.5
+    for i in range(matrix.shape[0]):
+        for j in range(matrix.shape[1]):
+            value = matrix[i, j]
+            text = f"{value * 100:.1f}%" if normalized else f"{int(value)}"
+            color = "white" if value > threshold else "black"
+            ax.text(j, i, text, ha="center", va="center", color=color, fontsize=11)
+    fig.savefig(output_path, dpi=120, bbox_inches="tight")
+    plt.close(fig)
+
+
+def plot_pr_curve(
+    y_true: np.ndarray,
+    scores: np.ndarray,
+    output_path: Path,
+    title: str = "PR",
+) -> None:
+    """Render a precision-recall curve. Title includes AP if computable.
+
+    When ``y_true`` contains no positives, writes a placeholder figure
+    (blank axes + informative title) instead of calling sklearn, which
+    would return a degenerate curve.
+    """
+    fig, ax = plt.subplots(figsize=(5, 5))
+    ax.set_xlabel("recall")
+    ax.set_ylabel("precision")
+    ax.set_xlim(0, 1)
+    ax.set_ylim(0, 1)
+    if y_true.sum() > 0:
+        ap = float(average_precision_score(y_true, scores))
+        precision, recall, _ = precision_recall_curve(y_true, scores)
+        ax.plot(recall, precision)
+        ax.set_title(f"{title} (AP={ap:.3f})")
+    else:
+        ax.set_title(f"{title} (no positives)")
+    fig.savefig(output_path, dpi=120, bbox_inches="tight")
+    plt.close(fig)
+
+
+def plot_roc_curve(
+    y_true: np.ndarray,
+    scores: np.ndarray,
+    output_path: Path,
+    title: str = "ROC",
+) -> None:
+    """Render a ROC curve. Title includes AUC if computable.
+
+    When ``y_true`` contains only one class, writes a placeholder figure
+    (diagonal reference only + informative title) instead of calling
+    sklearn, whose ``roc_curve`` returns NaN on single-class input.
+    """
+    fig, ax = plt.subplots(figsize=(5, 5))
+    ax.plot([0, 1], [0, 1], "k--", alpha=0.4)
+    ax.set_xlabel("false positive rate")
+    ax.set_ylabel("true positive rate")
+    n_pos = int(y_true.sum())
+    if 0 < n_pos < len(y_true):
+        auc = float(roc_auc_score(y_true, scores))
+        fpr, tpr, _ = roc_curve(y_true, scores)
+        ax.plot(fpr, tpr)
+        ax.set_title(f"{title} (AUC={auc:.3f})")
+    else:
+        ax.set_title(f"{title} (single class)")
+    fig.savefig(output_path, dpi=120, bbox_inches="tight")
+    plt.close(fig)
diff --git a/experiments/temporal-models/tube-multiscale-fusion/src/tube_multiscale_fusion/fusion.py b/experiments/temporal-models/tube-multiscale-fusion/src/tube_multiscale_fusion/fusion.py
new file mode 100644
index 00000000..429da637
--- /dev/null
+++ b/experiments/temporal-models/tube-multiscale-fusion/src/tube_multiscale_fusion/fusion.py
@@ -0,0 +1,204 @@
+"""Fusion module: self-attention over local tubes + cross-attention with global.
+
+Takes the global context vector ``(B, D_global)`` and local tube embeddings
+``(B, N_tubes, D_local)`` plus a ``(B, N_tubes)`` validity mask, projects both
+to a shared ``d_fusion`` width, and runs ``num_layers`` decoder-style blocks:
+
+    1. Self-attention over local tube tokens (with key-padding mask).
+    2. Cross-attention: global token = query, local tubes = key/value.
+    3. FFN on the global token.
+
+Returns the updated ``(B, d_fusion)`` global representation.
+"""
+
+from __future__ import annotations
+
+import torch
+from torch import Tensor, nn
+
+
+class _FusionBlock(nn.Module):
+    """One layer: self-attn on locals + cross-attn (global=Q, locals=KV) + FFN."""
+
+    def __init__(
+        self,
+        d_model: int,
+        num_heads: int,
+        ffn_dim: int,
+        dropout: float,
+    ) -> None:
+        super().__init__()
+        # Self-attention over local tube tokens.
+        self.norm_local = nn.LayerNorm(d_model)
+        self.self_attn = nn.MultiheadAttention(
+            embed_dim=d_model,
+            num_heads=num_heads,
+            dropout=dropout,
+            batch_first=True,
+        )
+        self.norm_local_ffn = nn.LayerNorm(d_model)
+        self.local_ffn = nn.Sequential(
+            nn.Linear(d_model, ffn_dim),
+            nn.GELU(),
+            nn.Dropout(dropout),
+            nn.Linear(ffn_dim, d_model),
+            nn.Dropout(dropout),
+        )
+
+        # Cross-attention: global queries locals.
+        self.norm_q = nn.LayerNorm(d_model)
+        self.norm_kv = nn.LayerNorm(d_model)
+        self.cross_attn = nn.MultiheadAttention(
+            embed_dim=d_model,
+            num_heads=num_heads,
+            dropout=dropout,
+            batch_first=True,
+        )
+        self.norm_g_ffn = nn.LayerNorm(d_model)
+        self.g_ffn = nn.Sequential(
+            nn.Linear(d_model, ffn_dim),
+            nn.GELU(),
+            nn.Dropout(dropout),
+            nn.Linear(ffn_dim, d_model),
+            nn.Dropout(dropout),
+        )
+
+    def forward(
+        self,
+        global_tok: Tensor,
+        local_tokens: Tensor,
+        local_mask: Tensor,
+    ) -> tuple[Tensor, Tensor]:
+        """global_tok ``(B, 1, D)``, local_tokens ``(B, N, D)``, mask ``(B, N)``.
+
+        Returns updated ``(global_tok, local_tokens)``.
+        """
+        key_padding = ~local_mask  # True = ignore.
+
+        # Self-attention on local tube tokens.
+        h = self.norm_local(local_tokens)
+        attn_out, _ = self.self_attn(
+            h, h, h, key_padding_mask=key_padding, need_weights=False
+        )
+        local_tokens = local_tokens + attn_out
+        local_tokens = local_tokens + self.local_ffn(self.norm_local_ffn(local_tokens))
+
+        # Cross-attention: global queries locals.
+        q = self.norm_q(global_tok)
+        kv = self.norm_kv(local_tokens)
+        cross_out, _ = self.cross_attn(
+            q, kv, kv, key_padding_mask=key_padding, need_weights=False
+        )
+        global_tok = global_tok + cross_out
+        global_tok = global_tok + self.g_ffn(self.norm_g_ffn(global_tok))
+        return global_tok, local_tokens
+
+
+class FusionModule(nn.Module):
+    """Project global + local to ``d_fusion``, then stack ``num_layers`` blocks."""
+
+    def __init__(
+        self,
+        global_dim: int,
+        local_dim: int,
+        d_fusion: int,
+        num_layers: int,
+        num_heads: int,
+        ffn_dim: int,
+        dropout: float,
+    ) -> None:
+        super().__init__()
+        self.global_proj: nn.Module = (
+            nn.Identity() if global_dim == d_fusion else nn.Linear(global_dim, d_fusion)
+        )
+        self.local_proj: nn.Module = (
+            nn.Identity() if local_dim == d_fusion else nn.Linear(local_dim, d_fusion)
+        )
+        self.blocks = nn.ModuleList(
+            [
+                _FusionBlock(
+                    d_model=d_fusion,
+                    num_heads=num_heads,
+                    ffn_dim=ffn_dim,
+                    dropout=dropout,
+                )
+                for _ in range(num_layers)
+            ]
+        )
+        self.final_norm = nn.LayerNorm(d_fusion)
+        self.d_fusion = d_fusion
+
+    def forward(
+        self,
+        global_vec: Tensor,
+        local_tokens: Tensor,
+        local_mask: Tensor,
+    ) -> Tensor:
+        """``global_vec: (B, D_global)``, ``local_tokens: (B, N, D_local)``,
+        ``local_mask: (B, N)`` -> ``(B, d_fusion)``.
+
+        If a sample has *no* valid local tubes (all masked), key-padding would
+        produce NaNs in attention; we guard against that by treating empty
+        masks as fully visible (the global readout is then just self-mixed).
+        """
+        g = self.global_proj(global_vec).unsqueeze(1)  # (B, 1, D)
+        ltok = self.local_proj(local_tokens)
+        # Guard: if any row has zero valid locals, keep it visible to avoid NaN.
+        safe_mask = local_mask.clone()
+        empty_rows = ~safe_mask.any(dim=1)
+        if torch.any(empty_rows):
+            safe_mask[empty_rows] = True
+        for block in self.blocks:
+            g, ltok = block(g, ltok, safe_mask)
+        return self.final_norm(g.squeeze(1))
+
+
+class WeightedMeanFusion(nn.Module):
+    """Attention-free fusion ablation: weighted means instead of cross-attention.
+
+    Replaces :class:`FusionModule` (self-attn over tubes + global→locals
+    cross-attn) with two learned weighted means:
+
+    1. **Over local tubes:** a per-token score (Linear→scalar) is softmaxed over
+       the valid tubes to give a weighted mean ``local_pooled`` (i.e. learned
+       attention-pooling — a weighted mean, no token-token attention).
+    2. **Between branches:** a learnable 2-vector is softmaxed into
+       ``[w_global, w_local]`` and used to take a weighted mean of the global
+       vector and ``local_pooled``.
+
+    Same input/output signature as :class:`FusionModule` so it is a drop-in
+    fusion swap for the ablation.
+    """
+
+    def __init__(self, global_dim: int, local_dim: int, d_fusion: int) -> None:
+        super().__init__()
+        self.global_proj: nn.Module = (
+            nn.Identity() if global_dim == d_fusion else nn.Linear(global_dim, d_fusion)
+        )
+        self.local_proj: nn.Module = (
+            nn.Identity() if local_dim == d_fusion else nn.Linear(local_dim, d_fusion)
+        )
+        self.local_score = nn.Linear(d_fusion, 1)
+        self.branch_weights = nn.Parameter(torch.zeros(2))
+        self.final_norm = nn.LayerNorm(d_fusion)
+        self.d_fusion = d_fusion
+
+    def forward(
+        self,
+        global_vec: Tensor,
+        local_tokens: Tensor,
+        local_mask: Tensor,
+    ) -> Tensor:
+        g = self.global_proj(global_vec)  # (B, d)
+        lt = self.local_proj(local_tokens)  # (B, N, d)
+
+        scores = self.local_score(lt).squeeze(-1)  # (B, N)
+        scores = scores.masked_fill(~local_mask, float("-inf"))
+        weights = torch.softmax(scores, dim=1)
+        # All-masked rows softmax to NaN; zero them so local contributes nothing.
+        weights = torch.nan_to_num(weights, nan=0.0)
+        local_pooled = (weights.unsqueeze(-1) * lt).sum(dim=1)  # (B, d)
+
+        bw = torch.softmax(self.branch_weights, dim=0)  # (2,)
+        fused = bw[0] * g + bw[1] * local_pooled
+        return self.final_norm(fused)
diff --git a/experiments/temporal-models/tube-multiscale-fusion/src/tube_multiscale_fusion/global_branch.py b/experiments/temporal-models/tube-multiscale-fusion/src/tube_multiscale_fusion/global_branch.py
new file mode 100644
index 00000000..1670f224
--- /dev/null
+++ b/experiments/temporal-models/tube-multiscale-fusion/src/tube_multiscale_fusion/global_branch.py
@@ -0,0 +1,339 @@
+"""Global branch: per-frame DINOv2 + sequence transformer aggregator.
+
+Embeds each of ``T`` frames independently with a timm backbone (default
+DINOv2 ViT-S/14), then aggregates the resulting ``(B, T, D)`` sequence into
+a single ``(B, D)`` global context vector via a small Transformer encoder
+with a learnable ``[CLS]`` token.
+"""
+
+from __future__ import annotations
+
+import timm
+import torch
+from torch import Tensor, nn
+from torch.nn.utils.rnn import pack_padded_sequence
+
+
+class TimmBackbone(nn.Module):
+    """Per-frame feature extractor backed by a pretrained timm model.
+
+    Ported from ``bbox-tube-motion-fusion/temporal_classifier.py``.
+    Supports either fully-frozen mode or finetuning the last ``N`` blocks
+    (resolved per backbone family).
+    """
+
+    def __init__(
+        self,
+        name: str,
+        pretrained: bool = True,
+        finetune: bool = False,
+        finetune_last_n_blocks: int = 0,
+        global_pool: str = "token",
+        img_size: int | None = 224,
+    ) -> None:
+        super().__init__()
+        extra = {"img_size": img_size} if img_size is not None else {}
+        self.backbone = timm.create_model(
+            name,
+            pretrained=pretrained,
+            num_classes=0,
+            global_pool=global_pool,
+            **extra,
+        )
+        self.feat_dim: int = self.backbone.num_features
+        self.finetune = finetune
+        self.name = name
+
+        if not finetune:
+            for p in self.backbone.parameters():
+                p.requires_grad = False
+            self.backbone.eval()
+            return
+
+        for p in self.backbone.parameters():
+            p.requires_grad = False
+        self._unfreeze_last_n_blocks(finetune_last_n_blocks)
+
+    def _unfreeze_last_n_blocks(self, n: int) -> None:
+        if n <= 0:
+            return
+        name = self.name
+        if name.startswith("resnet"):
+            stages = [getattr(self.backbone, f"layer{i}") for i in range(1, 5)]
+        elif name.startswith("convnext"):
+            stages = list(self.backbone.stages)
+        elif name.startswith("vit_"):
+            stages = list(self.backbone.blocks)
+        else:
+            stage_names = [n_ for n_, _ in self.backbone.named_children()]
+            raise NotImplementedError(
+                f"finetune=True is not implemented for backbone family "
+                f"{name!r}. Top-level children: {stage_names}."
+            )
+        for stage in stages[-n:]:
+            for p in stage.parameters():
+                p.requires_grad = True
+
+    def train(self, mode: bool = True) -> TimmBackbone:
+        super().train(mode)
+        if not self.finetune:
+            self.backbone.eval()
+        else:
+            for module in self.backbone.modules():
+                if not any(p.requires_grad for p in module.parameters(recurse=False)):
+                    has_trainable_descendant = any(
+                        p.requires_grad for p in module.parameters()
+                    )
+                    if not has_trainable_descendant:
+                        module.eval()
+        return self
+
+    def forward(self, x: Tensor) -> Tensor:
+        if not self.finetune:
+            with torch.no_grad():
+                return self.backbone(x)
+        return self.backbone(x)
+
+
+class GlobalAggregator(nn.Module):
+    """Aggregates a ``(B, T, D)`` per-frame sequence into a single ``(B, D)`` vector.
+
+    Architecture: learnable ``[CLS]`` token + learned positional embeddings +
+    Transformer encoder. The CLS token's final state is returned.
+    """
+
+    def __init__(
+        self,
+        feat_dim: int,
+        num_layers: int,
+        num_heads: int,
+        ffn_dim: int,
+        dropout: float,
+        max_frames: int,
+    ) -> None:
+        super().__init__()
+        self.cls_token = nn.Parameter(torch.zeros(1, 1, feat_dim))
+        nn.init.trunc_normal_(self.cls_token, std=0.02)
+        self.pos_embed = nn.Parameter(torch.zeros(1, max_frames + 1, feat_dim))
+        nn.init.trunc_normal_(self.pos_embed, std=0.02)
+        encoder_layer = nn.TransformerEncoderLayer(
+            d_model=feat_dim,
+            nhead=num_heads,
+            dim_feedforward=ffn_dim,
+            dropout=dropout,
+            batch_first=True,
+            norm_first=True,
+            activation="gelu",
+        )
+        self.encoder = nn.TransformerEncoder(encoder_layer, num_layers=num_layers)
+        self.max_frames = max_frames
+
+    def forward(self, feats: Tensor, mask: Tensor) -> Tensor:
+        b, t, _ = feats.shape
+        if t > self.max_frames:
+            raise ValueError(
+                f"GlobalAggregator received T={t} > max_frames={self.max_frames}"
+            )
+        cls = self.cls_token.expand(b, 1, -1)
+        x = torch.cat([cls, feats], dim=1)
+        x = x + self.pos_embed[:, : t + 1, :]
+        cls_real = torch.ones(b, 1, dtype=torch.bool, device=mask.device)
+        real_mask = torch.cat([cls_real, mask], dim=1)
+        key_padding_mask = ~real_mask
+        out = self.encoder(x, src_key_padding_mask=key_padding_mask)
+        return out[:, 0, :]
+
+
+class RNNAggregator(nn.Module):
+    """LSTM/GRU over the per-frame sequence; returns the final hidden state.
+
+    Uses packed sequences so padded frames are ignored. ``hidden_size`` is set
+    to ``feat_dim`` so the output matches the rest of the architecture.
+    """
+
+    def __init__(
+        self,
+        feat_dim: int,
+        kind: str,
+        num_layers: int = 1,
+        dropout: float = 0.0,
+    ) -> None:
+        super().__init__()
+        rnn_cls = {"lstm": nn.LSTM, "gru": nn.GRU}[kind]
+        self.kind = kind
+        self.rnn = rnn_cls(
+            input_size=feat_dim,
+            hidden_size=feat_dim,
+            num_layers=num_layers,
+            batch_first=True,
+            dropout=dropout if num_layers > 1 else 0.0,
+        )
+
+    def forward(self, feats: Tensor, mask: Tensor) -> Tensor:
+        lengths = mask.sum(dim=1).clamp(min=1).cpu()
+        packed = pack_padded_sequence(
+            feats, lengths, batch_first=True, enforce_sorted=False
+        )
+        if self.kind == "lstm":
+            _, (h_n, _) = self.rnn(packed)
+        else:
+            _, h_n = self.rnn(packed)
+        return h_n[-1]  # (B, feat_dim)
+
+
+class MLPAggregator(nn.Module):
+    """Flatten the (masked) ``T×D`` sequence and run an MLP -> ``(B, D)``.
+
+    Padded frames are zeroed before flattening. Sees the whole sequence at once
+    but has no inductive bias for temporal order beyond position-in-vector.
+    """
+
+    def __init__(
+        self,
+        feat_dim: int,
+        max_frames: int,
+        hidden: int = 1024,
+        dropout: float = 0.1,
+    ) -> None:
+        super().__init__()
+        self.max_frames = max_frames
+        self.net = nn.Sequential(
+            nn.Linear(feat_dim * max_frames, hidden),
+            nn.GELU(),
+            nn.Dropout(dropout),
+            nn.Linear(hidden, feat_dim),
+        )
+
+    def forward(self, feats: Tensor, mask: Tensor) -> Tensor:
+        feats = feats * mask.unsqueeze(-1).to(feats.dtype)
+        return self.net(feats.reshape(feats.shape[0], -1))
+
+
+class Conv1DAggregator(nn.Module):
+    """1D temporal convolutions over the (B, D, T) sequence + masked mean pool."""
+
+    def __init__(
+        self,
+        feat_dim: int,
+        num_layers: int = 2,
+        kernel: int = 3,
+        dropout: float = 0.0,
+    ) -> None:
+        super().__init__()
+        layers: list[nn.Module] = []
+        for _ in range(num_layers):
+            layers += [
+                nn.Conv1d(feat_dim, feat_dim, kernel, padding=kernel // 2),
+                nn.GELU(),
+                nn.Dropout(dropout),
+            ]
+        self.conv = nn.Sequential(*layers)
+
+    def forward(self, feats: Tensor, mask: Tensor) -> Tensor:
+        x = self.conv(feats.transpose(1, 2)).transpose(1, 2)  # (B, T, D)
+        m = mask.unsqueeze(-1).to(x.dtype)
+        return (x * m).sum(dim=1) / m.sum(dim=1).clamp(min=1.0)
+
+
+class LinearWeightedAvgAggregator(nn.Module):
+    """Per-frame Linear projection + learned weighted average over time.
+
+    A lightweight attention-pool with no token-token interaction: each frame is
+    projected, scored to a scalar, softmax-normalized over the valid frames, and
+    combined by weighted mean.
+    """
+
+    def __init__(self, feat_dim: int) -> None:
+        super().__init__()
+        self.proj = nn.Linear(feat_dim, feat_dim)
+        self.score = nn.Linear(feat_dim, 1)
+
+    def forward(self, feats: Tensor, mask: Tensor) -> Tensor:
+        h = self.proj(feats)  # (B, T, D)
+        s = self.score(h).squeeze(-1)  # (B, T)
+        s = s.masked_fill(~mask, float("-inf"))
+        w = torch.softmax(s, dim=1)
+        w = torch.nan_to_num(w, nan=0.0)
+        return (w.unsqueeze(-1) * h).sum(dim=1)  # (B, D)
+
+
+def build_aggregator(
+    kind: str,
+    feat_dim: int,
+    max_frames: int,
+    *,
+    num_layers: int,
+    num_heads: int,
+    ffn_dim: int,
+    dropout: float,
+    rnn_layers: int = 1,
+    mlp_hidden: int = 1024,
+    conv_layers: int = 2,
+    conv_kernel: int = 3,
+) -> nn.Module:
+    """Construct a temporal aggregator ``(B, T, D), mask -> (B, D)`` by name."""
+    if kind == "transformer":
+        return GlobalAggregator(
+            feat_dim=feat_dim,
+            num_layers=num_layers,
+            num_heads=num_heads,
+            ffn_dim=ffn_dim,
+            dropout=dropout,
+            max_frames=max_frames,
+        )
+    if kind in ("lstm", "gru"):
+        return RNNAggregator(
+            feat_dim, kind=kind, num_layers=rnn_layers, dropout=dropout
+        )
+    if kind == "mlp":
+        return MLPAggregator(feat_dim, max_frames, hidden=mlp_hidden, dropout=dropout)
+    if kind == "conv1d":
+        return Conv1DAggregator(
+            feat_dim, num_layers=conv_layers, kernel=conv_kernel, dropout=dropout
+        )
+    if kind == "linear_wavg":
+        return LinearWeightedAvgAggregator(feat_dim)
+    raise ValueError(f"unknown aggregator kind {kind!r}")
+
+
+class GlobalBranch(nn.Module):
+    """DINOv2 per-frame embeddings + a temporal aggregator -> ``(B, D)``."""
+
+    def __init__(
+        self,
+        backbone: str,
+        finetune: bool,
+        finetune_last_n_blocks: int,
+        max_frames: int,
+        aggregator_num_layers: int,
+        aggregator_num_heads: int,
+        aggregator_ffn_dim: int,
+        aggregator_dropout: float,
+        aggregator_kind: str = "transformer",
+        img_size: int = 224,
+        pretrained: bool = True,
+    ) -> None:
+        super().__init__()
+        self.backbone = TimmBackbone(
+            name=backbone,
+            pretrained=pretrained,
+            finetune=finetune,
+            finetune_last_n_blocks=finetune_last_n_blocks,
+            img_size=img_size,
+        )
+        self.feat_dim = self.backbone.feat_dim
+        self.aggregator = build_aggregator(
+            aggregator_kind,
+            feat_dim=self.feat_dim,
+            max_frames=max_frames,
+            num_layers=aggregator_num_layers,
+            num_heads=aggregator_num_heads,
+            ffn_dim=aggregator_ffn_dim,
+            dropout=aggregator_dropout,
+        )
+
+    def forward(self, patches: Tensor, mask: Tensor) -> Tensor:
+        b, t, c, h, w = patches.shape
+        feats = self.backbone(patches.reshape(b * t, c, h, w))
+        feats = feats.reshape(b, t, self.feat_dim)
+        return self.aggregator(feats, mask)
diff --git a/experiments/temporal-models/tube-multiscale-fusion/src/tube_multiscale_fusion/lit_ablation.py b/experiments/temporal-models/tube-multiscale-fusion/src/tube_multiscale_fusion/lit_ablation.py
new file mode 100644
index 00000000..f3fcc0f0
--- /dev/null
+++ b/experiments/temporal-models/tube-multiscale-fusion/src/tube_multiscale_fusion/lit_ablation.py
@@ -0,0 +1,345 @@
+"""Lightning wrapper for the no-temporal-module ablation classifier.
+
+Mirrors ``LitTubeMultiscaleClassifier`` exactly (BCE, AdamW, cosine warmup,
+F1 metric logging) so the two models are directly comparable. There is no
+backbone to fine-tune here (the global DINOv2 branch is removed), so a single
+parameter group is used.
+"""
+
+from __future__ import annotations
+
+import math
+
+import lightning as L
+import torch
+from torch import Tensor
+
+from .ablation_classifier import (
+    AblationNoSpatialClassifier,
+    AblationNoTemporalClassifier,
+    AblationWeightedMeanClassifier,
+)
+
+
+class LitAblationNoTemporal(L.LightningModule):
+    def __init__(
+        self,
+        grid_size: int,
+        cell_size: int,
+        tube_length: int,
+        temporal_stride: int,
+        local_t_kernel: int,
+        local_h_patch: int,
+        local_w_patch: int,
+        local_embed_dim: int,
+        local_num_layers: int,
+        local_num_heads: int,
+        local_ffn_dim: int,
+        local_dropout: float,
+        d_fusion: int,
+        fusion_num_layers: int,
+        fusion_num_heads: int,
+        fusion_ffn_dim: int,
+        fusion_dropout: float,
+        head_hidden_dim: int,
+        head_dropout: float,
+        query_dim: int,
+        learning_rate: float,
+        weight_decay: float,
+        encoder_kind: str = "tubelet_transformer",
+        use_cosine_warmup: bool = False,
+        warmup_frac: float = 0.05,
+    ) -> None:
+        super().__init__()
+        self.save_hyperparameters()
+        self.model = AblationNoTemporalClassifier(
+            grid_size=grid_size,
+            cell_size=cell_size,
+            tube_length=tube_length,
+            temporal_stride=temporal_stride,
+            local_t_kernel=local_t_kernel,
+            local_h_patch=local_h_patch,
+            local_w_patch=local_w_patch,
+            local_embed_dim=local_embed_dim,
+            local_num_layers=local_num_layers,
+            local_num_heads=local_num_heads,
+            local_ffn_dim=local_ffn_dim,
+            local_dropout=local_dropout,
+            d_fusion=d_fusion,
+            fusion_num_layers=fusion_num_layers,
+            fusion_num_heads=fusion_num_heads,
+            fusion_ffn_dim=fusion_ffn_dim,
+            fusion_dropout=fusion_dropout,
+            head_hidden_dim=head_hidden_dim,
+            head_dropout=head_dropout,
+            query_dim=query_dim,
+            encoder_kind=encoder_kind,
+        )
+        self.loss_fn = torch.nn.BCEWithLogitsLoss()
+        self.learning_rate = learning_rate
+        self.weight_decay = weight_decay
+        self.use_cosine_warmup = use_cosine_warmup
+        self.warmup_frac = warmup_frac
+        self._val_preds: list[float] = []
+        self._val_labels: list[float] = []
+
+    def forward(self, patches: Tensor, mask: Tensor) -> Tensor:
+        return self.model(patches, mask)
+
+    def training_step(self, batch: dict, batch_idx: int) -> Tensor:
+        logits = self(batch["patches"], batch["mask"])
+        loss = self.loss_fn(logits, batch["label"])
+        self.log("train/loss", loss, prog_bar=True, batch_size=logits.shape[0])
+        return loss
+
+    def validation_step(self, batch: dict, batch_idx: int) -> None:
+        logits = self(batch["patches"], batch["mask"])
+        loss = self.loss_fn(logits, batch["label"])
+        probs = torch.sigmoid(logits).detach().cpu()
+        labels = batch["label"].detach().cpu()
+        self._val_preds.extend(probs.tolist())
+        self._val_labels.extend(labels.tolist())
+        self.log("val/loss", loss, prog_bar=True, batch_size=logits.shape[0])
+
+    def on_validation_epoch_end(self) -> None:
+        if not self._val_preds:
+            return
+        probs = torch.tensor(self._val_preds)
+        labels = torch.tensor(self._val_labels)
+        preds = (probs > 0.5).float()
+        tp = ((preds == 1) & (labels == 1)).sum().float()
+        fp = ((preds == 1) & (labels == 0)).sum().float()
+        fn = ((preds == 0) & (labels == 1)).sum().float()
+        tn = ((preds == 0) & (labels == 0)).sum().float()
+        acc = (tp + tn) / (tp + tn + fp + fn).clamp(min=1)
+        prec = tp / (tp + fp).clamp(min=1)
+        rec = tp / (tp + fn).clamp(min=1)
+        f1 = 2 * prec * rec / (prec + rec).clamp(min=1e-8)
+        self.log("val/accuracy", acc, prog_bar=True)
+        self.log("val/precision", prec)
+        self.log("val/recall", rec)
+        self.log("val/f1", f1, prog_bar=True)
+        self._val_preds.clear()
+        self._val_labels.clear()
+
+    def configure_optimizers(self):
+        optimizer = torch.optim.AdamW(
+            [p for p in self.model.parameters() if p.requires_grad],
+            lr=self.learning_rate,
+            weight_decay=self.weight_decay,
+        )
+        if not self.use_cosine_warmup:
+            return optimizer
+
+        total_steps = int(self.trainer.estimated_stepping_batches)
+        warmup_steps = max(1, int(total_steps * self.warmup_frac))
+
+        def lr_lambda(step: int) -> float:
+            if step < warmup_steps:
+                return step / warmup_steps
+            progress = (step - warmup_steps) / max(1, total_steps - warmup_steps)
+            return 0.5 * (1.0 + math.cos(math.pi * progress))
+
+        scheduler = torch.optim.lr_scheduler.LambdaLR(optimizer, lr_lambda)
+        return {
+            "optimizer": optimizer,
+            "lr_scheduler": {"scheduler": scheduler, "interval": "step"},
+        }
+
+
+class LitAblationGlobal(L.LightningModule):
+    """Lightning wrapper for the global-branch ablations.
+
+    Handles two variants that both retain the global DINOv2 branch (and thus a
+    fine-tunable backbone with a separate LR):
+
+    - ``no_spatial``:    :class:`AblationNoSpatialClassifier` (global only)
+    - ``weighted_mean``: :class:`AblationWeightedMeanClassifier`
+      (cross-attention fusion → weighted means)
+    """
+
+    def __init__(
+        self,
+        variant: str,
+        backbone: str,
+        max_frames: int,
+        global_aggregator_num_layers: int,
+        global_aggregator_num_heads: int,
+        global_aggregator_ffn_dim: int,
+        global_aggregator_dropout: float,
+        head_hidden_dim: int,
+        head_dropout: float,
+        learning_rate: float,
+        weight_decay: float,
+        # local + fusion (only used by weighted_mean)
+        grid_size: int = 2,
+        cell_size: int = 112,
+        tube_length: int = 4,
+        temporal_stride: int = 2,
+        local_t_kernel: int = 2,
+        local_h_patch: int = 14,
+        local_w_patch: int = 14,
+        local_embed_dim: int = 384,
+        local_num_layers: int = 2,
+        local_num_heads: int = 6,
+        local_ffn_dim: int = 1536,
+        local_dropout: float = 0.1,
+        d_fusion: int = 384,
+        finetune: bool = False,
+        finetune_last_n_blocks: int = 0,
+        backbone_lr: float | None = None,
+        aggregator_kind: str = "transformer",
+        img_size: int = 224,
+        pretrained: bool = True,
+        use_cosine_warmup: bool = False,
+        warmup_frac: float = 0.05,
+    ) -> None:
+        super().__init__()
+        self.save_hyperparameters()
+        if variant == "no_spatial":
+            self.model: torch.nn.Module = AblationNoSpatialClassifier(
+                backbone=backbone,
+                finetune=finetune,
+                finetune_last_n_blocks=finetune_last_n_blocks,
+                max_frames=max_frames,
+                global_aggregator_num_layers=global_aggregator_num_layers,
+                global_aggregator_num_heads=global_aggregator_num_heads,
+                global_aggregator_ffn_dim=global_aggregator_ffn_dim,
+                global_aggregator_dropout=global_aggregator_dropout,
+                head_hidden_dim=head_hidden_dim,
+                head_dropout=head_dropout,
+                aggregator_kind=aggregator_kind,
+                img_size=img_size,
+                pretrained=pretrained,
+            )
+        elif variant == "weighted_mean":
+            self.model = AblationWeightedMeanClassifier(
+                backbone=backbone,
+                finetune=finetune,
+                finetune_last_n_blocks=finetune_last_n_blocks,
+                max_frames=max_frames,
+                global_aggregator_num_layers=global_aggregator_num_layers,
+                global_aggregator_num_heads=global_aggregator_num_heads,
+                global_aggregator_ffn_dim=global_aggregator_ffn_dim,
+                global_aggregator_dropout=global_aggregator_dropout,
+                grid_size=grid_size,
+                cell_size=cell_size,
+                tube_length=tube_length,
+                temporal_stride=temporal_stride,
+                local_t_kernel=local_t_kernel,
+                local_h_patch=local_h_patch,
+                local_w_patch=local_w_patch,
+                local_embed_dim=local_embed_dim,
+                local_num_layers=local_num_layers,
+                local_num_heads=local_num_heads,
+                local_ffn_dim=local_ffn_dim,
+                local_dropout=local_dropout,
+                d_fusion=d_fusion,
+                head_hidden_dim=head_hidden_dim,
+                head_dropout=head_dropout,
+                img_size=img_size,
+                pretrained=pretrained,
+            )
+        else:
+            raise ValueError(f"unknown variant {variant!r}")
+
+        self.loss_fn = torch.nn.BCEWithLogitsLoss()
+        self.learning_rate = learning_rate
+        self.weight_decay = weight_decay
+        self.finetune = finetune
+        self.backbone_lr = backbone_lr
+        self.use_cosine_warmup = use_cosine_warmup
+        self.warmup_frac = warmup_frac
+        self._val_preds: list[float] = []
+        self._val_labels: list[float] = []
+
+    def forward(self, patches: Tensor, mask: Tensor) -> Tensor:
+        return self.model(patches, mask)
+
+    def training_step(self, batch: dict, batch_idx: int) -> Tensor:
+        logits = self(batch["patches"], batch["mask"])
+        loss = self.loss_fn(logits, batch["label"])
+        self.log("train/loss", loss, prog_bar=True, batch_size=logits.shape[0])
+        return loss
+
+    def validation_step(self, batch: dict, batch_idx: int) -> None:
+        logits = self(batch["patches"], batch["mask"])
+        loss = self.loss_fn(logits, batch["label"])
+        self._val_preds.extend(torch.sigmoid(logits).detach().cpu().tolist())
+        self._val_labels.extend(batch["label"].detach().cpu().tolist())
+        self.log("val/loss", loss, prog_bar=True, batch_size=logits.shape[0])
+
+    def on_validation_epoch_end(self) -> None:
+        if not self._val_preds:
+            return
+        probs = torch.tensor(self._val_preds)
+        labels = torch.tensor(self._val_labels)
+        preds = (probs > 0.5).float()
+        tp = ((preds == 1) & (labels == 1)).sum().float()
+        fp = ((preds == 1) & (labels == 0)).sum().float()
+        fn = ((preds == 0) & (labels == 1)).sum().float()
+        tn = ((preds == 0) & (labels == 0)).sum().float()
+        acc = (tp + tn) / (tp + tn + fp + fn).clamp(min=1)
+        prec = tp / (tp + fp).clamp(min=1)
+        rec = tp / (tp + fn).clamp(min=1)
+        f1 = 2 * prec * rec / (prec + rec).clamp(min=1e-8)
+        self.log("val/accuracy", acc, prog_bar=True)
+        self.log("val/precision", prec)
+        self.log("val/recall", rec)
+        self.log("val/f1", f1, prog_bar=True)
+        self._val_preds.clear()
+        self._val_labels.clear()
+
+    def _non_backbone_params(self) -> list[torch.nn.Parameter]:
+        backbone_ids = {id(p) for p in self.model.global_branch.backbone.parameters()}
+        return [
+            p
+            for p in self.model.parameters()
+            if p.requires_grad and id(p) not in backbone_ids
+        ]
+
+    def configure_optimizers(self):
+        non_backbone = self._non_backbone_params()
+        if not self.finetune:
+            optimizer = torch.optim.AdamW(
+                non_backbone, lr=self.learning_rate, weight_decay=self.weight_decay
+            )
+        else:
+            if self.backbone_lr is None:
+                raise ValueError("backbone_lr must be set when finetune=True")
+            backbone_params = [
+                p
+                for p in self.model.global_branch.backbone.parameters()
+                if p.requires_grad
+            ]
+            optimizer = torch.optim.AdamW(
+                [
+                    {
+                        "params": non_backbone,
+                        "lr": self.learning_rate,
+                        "weight_decay": self.weight_decay,
+                    },
+                    {
+                        "params": backbone_params,
+                        "lr": self.backbone_lr,
+                        "weight_decay": self.weight_decay,
+                    },
+                ]
+            )
+        if not self.use_cosine_warmup:
+            return optimizer
+
+        total_steps = int(self.trainer.estimated_stepping_batches)
+        warmup_steps = max(1, int(total_steps * self.warmup_frac))
+
+        def lr_lambda(step: int) -> float:
+            if step < warmup_steps:
+                return step / warmup_steps
+            progress = (step - warmup_steps) / max(1, total_steps - warmup_steps)
+            return 0.5 * (1.0 + math.cos(math.pi * progress))
+
+        scheduler = torch.optim.lr_scheduler.LambdaLR(optimizer, lr_lambda)
+        return {
+            "optimizer": optimizer,
+            "lr_scheduler": {"scheduler": scheduler, "interval": "step"},
+        }
diff --git a/experiments/temporal-models/tube-multiscale-fusion/src/tube_multiscale_fusion/lit_module.py b/experiments/temporal-models/tube-multiscale-fusion/src/tube_multiscale_fusion/lit_module.py
new file mode 100644
index 00000000..fd2537fa
--- /dev/null
+++ b/experiments/temporal-models/tube-multiscale-fusion/src/tube_multiscale_fusion/lit_module.py
@@ -0,0 +1,205 @@
+"""PyTorch Lightning wrapper around :class:`TubeMultiscaleClassifier`."""
+
+from __future__ import annotations
+
+import math
+
+import lightning as L
+import torch
+from torch import Tensor
+
+from .classifier import TubeMultiscaleClassifier
+
+
+class LitTubeMultiscaleClassifier(L.LightningModule):
+    """BCE loss; AdamW with two param groups when finetuning the backbone.
+
+    Logs train/val loss, accuracy, precision, recall, F1.
+    """
+
+    def __init__(
+        self,
+        backbone: str,
+        max_frames: int,
+        # Global aggregator
+        global_aggregator_num_layers: int,
+        global_aggregator_num_heads: int,
+        global_aggregator_ffn_dim: int,
+        global_aggregator_dropout: float,
+        # Local branch
+        grid_size: int,
+        cell_size: int,
+        tube_length: int,
+        temporal_stride: int,
+        local_t_kernel: int,
+        local_h_patch: int,
+        local_w_patch: int,
+        local_embed_dim: int,
+        local_num_layers: int,
+        local_num_heads: int,
+        local_ffn_dim: int,
+        local_dropout: float,
+        # Fusion
+        d_fusion: int,
+        fusion_num_layers: int,
+        fusion_num_heads: int,
+        fusion_ffn_dim: int,
+        fusion_dropout: float,
+        # Head
+        head_hidden_dim: int,
+        head_dropout: float,
+        # Training
+        learning_rate: float,
+        weight_decay: float,
+        finetune: bool = False,
+        finetune_last_n_blocks: int = 0,
+        backbone_lr: float | None = None,
+        aggregator_kind: str = "transformer",
+        encoder_kind: str = "tubelet_transformer",
+        img_size: int = 224,
+        pretrained: bool = True,
+        use_cosine_warmup: bool = False,
+        warmup_frac: float = 0.05,
+    ) -> None:
+        super().__init__()
+        self.save_hyperparameters()
+        self.model = TubeMultiscaleClassifier(
+            backbone=backbone,
+            finetune=finetune,
+            finetune_last_n_blocks=finetune_last_n_blocks,
+            max_frames=max_frames,
+            global_aggregator_num_layers=global_aggregator_num_layers,
+            global_aggregator_num_heads=global_aggregator_num_heads,
+            global_aggregator_ffn_dim=global_aggregator_ffn_dim,
+            global_aggregator_dropout=global_aggregator_dropout,
+            grid_size=grid_size,
+            cell_size=cell_size,
+            tube_length=tube_length,
+            temporal_stride=temporal_stride,
+            local_t_kernel=local_t_kernel,
+            local_h_patch=local_h_patch,
+            local_w_patch=local_w_patch,
+            local_embed_dim=local_embed_dim,
+            local_num_layers=local_num_layers,
+            local_num_heads=local_num_heads,
+            local_ffn_dim=local_ffn_dim,
+            local_dropout=local_dropout,
+            d_fusion=d_fusion,
+            fusion_num_layers=fusion_num_layers,
+            fusion_num_heads=fusion_num_heads,
+            fusion_ffn_dim=fusion_ffn_dim,
+            fusion_dropout=fusion_dropout,
+            head_hidden_dim=head_hidden_dim,
+            head_dropout=head_dropout,
+            aggregator_kind=aggregator_kind,
+            encoder_kind=encoder_kind,
+            img_size=img_size,
+            pretrained=pretrained,
+        )
+        self.loss_fn = torch.nn.BCEWithLogitsLoss()
+        self.learning_rate = learning_rate
+        self.weight_decay = weight_decay
+        self.finetune = finetune
+        self.backbone_lr = backbone_lr
+        self.use_cosine_warmup = use_cosine_warmup
+        self.warmup_frac = warmup_frac
+        self._val_preds: list[float] = []
+        self._val_labels: list[float] = []
+
+    def forward(self, patches: Tensor, mask: Tensor) -> Tensor:
+        return self.model(patches, mask)
+
+    def training_step(self, batch: dict, batch_idx: int) -> Tensor:
+        logits = self(batch["patches"], batch["mask"])
+        loss = self.loss_fn(logits, batch["label"])
+        self.log("train/loss", loss, prog_bar=True, batch_size=logits.shape[0])
+        return loss
+
+    def validation_step(self, batch: dict, batch_idx: int) -> None:
+        logits = self(batch["patches"], batch["mask"])
+        loss = self.loss_fn(logits, batch["label"])
+        probs = torch.sigmoid(logits).detach().cpu()
+        labels = batch["label"].detach().cpu()
+        self._val_preds.extend(probs.tolist())
+        self._val_labels.extend(labels.tolist())
+        self.log("val/loss", loss, prog_bar=True, batch_size=logits.shape[0])
+
+    def on_validation_epoch_end(self) -> None:
+        if not self._val_preds:
+            return
+        probs = torch.tensor(self._val_preds)
+        labels = torch.tensor(self._val_labels)
+        preds = (probs > 0.5).float()
+        tp = ((preds == 1) & (labels == 1)).sum().float()
+        fp = ((preds == 1) & (labels == 0)).sum().float()
+        fn = ((preds == 0) & (labels == 1)).sum().float()
+        tn = ((preds == 0) & (labels == 0)).sum().float()
+        acc = (tp + tn) / (tp + tn + fp + fn).clamp(min=1)
+        prec = tp / (tp + fp).clamp(min=1)
+        rec = tp / (tp + fn).clamp(min=1)
+        f1 = 2 * prec * rec / (prec + rec).clamp(min=1e-8)
+        self.log("val/accuracy", acc, prog_bar=True)
+        self.log("val/precision", prec)
+        self.log("val/recall", rec)
+        self.log("val/f1", f1, prog_bar=True)
+        self._val_preds.clear()
+        self._val_labels.clear()
+
+    def _trainable_non_backbone_params(self) -> list[torch.nn.Parameter]:
+        backbone_param_ids = {
+            id(p) for p in self.model.global_branch.backbone.parameters()
+        }
+        return [
+            p
+            for p in self.model.parameters()
+            if p.requires_grad and id(p) not in backbone_param_ids
+        ]
+
+    def configure_optimizers(self):
+        non_backbone = self._trainable_non_backbone_params()
+        if not self.finetune:
+            optimizer = torch.optim.AdamW(
+                non_backbone,
+                lr=self.learning_rate,
+                weight_decay=self.weight_decay,
+            )
+        else:
+            if self.backbone_lr is None:
+                raise ValueError("backbone_lr must be set when finetune=True")
+            backbone_params = [
+                p
+                for p in self.model.global_branch.backbone.parameters()
+                if p.requires_grad
+            ]
+            optimizer = torch.optim.AdamW(
+                [
+                    {
+                        "params": non_backbone,
+                        "lr": self.learning_rate,
+                        "weight_decay": self.weight_decay,
+                    },
+                    {
+                        "params": backbone_params,
+                        "lr": self.backbone_lr,
+                        "weight_decay": self.weight_decay,
+                    },
+                ]
+            )
+
+        if not self.use_cosine_warmup:
+            return optimizer
+
+        total_steps = int(self.trainer.estimated_stepping_batches)
+        warmup_steps = max(1, int(total_steps * self.warmup_frac))
+
+        def lr_lambda(step: int) -> float:
+            if step < warmup_steps:
+                return step / warmup_steps
+            progress = (step - warmup_steps) / max(1, total_steps - warmup_steps)
+            return 0.5 * (1.0 + math.cos(math.pi * progress))
+
+        scheduler = torch.optim.lr_scheduler.LambdaLR(optimizer, lr_lambda)
+        return {
+            "optimizer": optimizer,
+            "lr_scheduler": {"scheduler": scheduler, "interval": "step"},
+        }
diff --git a/experiments/temporal-models/tube-multiscale-fusion/src/tube_multiscale_fusion/local_branch.py b/experiments/temporal-models/tube-multiscale-fusion/src/tube_multiscale_fusion/local_branch.py
new file mode 100644
index 00000000..35c4958b
--- /dev/null
+++ b/experiments/temporal-models/tube-multiscale-fusion/src/tube_multiscale_fusion/local_branch.py
@@ -0,0 +1,539 @@
+"""Local branch: spatio-temporal tube extraction + per-tube video transformer.
+
+A 16-frame sequence of ``(3, H, W)`` patches is decomposed into a grid of
+``grid_size x grid_size`` spatial cells of side ``cell_size``, then sliced
+along time into overlapping windows of length ``tube_length`` with stride
+``temporal_stride``. Each resulting ``(3, tube_length, cell_size, cell_size)``
+tube is embedded into a single vector by a small video transformer
+(tubelet patch embed -> learnable [CLS] + pos embed -> self-attention).
+"""
+
+from __future__ import annotations
+
+import torch
+import torch.nn.functional as F
+from torch import Tensor, nn
+from torchvision.models.video import R3D_18_Weights, r3d_18
+
+_KINETICS_MEAN = (0.43216, 0.394666, 0.37645)
+_KINETICS_STD = (0.22803, 0.22145, 0.216989)
+_IMAGENET_MEAN = (0.485, 0.456, 0.406)
+_IMAGENET_STD = (0.229, 0.224, 0.225)
+
+
+def n_temporal_windows(num_frames: int, tube_length: int, temporal_stride: int) -> int:
+    """Number of fully-contained sliding windows over ``num_frames``."""
+    if num_frames < tube_length:
+        return 0
+    return (num_frames - tube_length) // temporal_stride + 1
+
+
+def extract_tubes(
+    patches: Tensor,
+    grid_size: int,
+    cell_size: int,
+    tube_length: int,
+    temporal_stride: int,
+) -> Tensor:
+    """Decompose ``(B, T, 3, H, W)`` patches into spatio-temporal tubes.
+
+    Args:
+        patches: ``(B, T, 3, H, W)`` RGB tensors. Requires
+            ``H == W == grid_size * cell_size``.
+        grid_size: Number of spatial cells per axis (e.g. 4).
+        cell_size: Side length of each spatial cell in pixels.
+        tube_length: Number of frames per temporal window.
+        temporal_stride: Frame stride between consecutive windows.
+
+    Returns:
+        ``(B, N_tubes, 3, tube_length, cell_size, cell_size)`` where
+        ``N_tubes = n_windows * grid_size**2``. Tubes are laid out as
+        ``[(window_0, cell_(0,0)), (window_0, cell_(0,1)), ...]``.
+    """
+    b, t, c, h, w = patches.shape
+    expected = grid_size * cell_size
+    if h != expected or w != expected:
+        raise ValueError(
+            f"extract_tubes expects H = W = grid_size * cell_size = {expected}, "
+            f"got H={h}, W={w}"
+        )
+    n_windows = n_temporal_windows(t, tube_length, temporal_stride)
+    if n_windows == 0:
+        raise ValueError(
+            f"extract_tubes: T={t} is shorter than tube_length={tube_length}"
+        )
+
+    # Temporal sliding windows: (B, n_windows, 3, H, W, tube_length)
+    temporal = patches.unfold(1, tube_length, temporal_stride)
+    # Move the unfold-appended tube_length axis next to the channel dim:
+    # -> (B, n_windows, 3, tube_length, H, W)
+    temporal = temporal.permute(0, 1, 2, 5, 3, 4).contiguous()
+
+    # Spatial unfold along H and W:
+    # -> (B, n_windows, 3, tube_length, grid_size, grid_size, cell_size, cell_size)
+    spatial = temporal.unfold(4, cell_size, cell_size).unfold(5, cell_size, cell_size)
+
+    # Collapse spatial grid -> tube axis. Reorder so each (window, cell) is one tube:
+    # (B, n_windows, grid_size, grid_size, 3, tube_length, cell_size, cell_size)
+    spatial = spatial.permute(0, 1, 4, 5, 2, 3, 6, 7).contiguous()
+    n_tubes = n_windows * grid_size * grid_size
+    return spatial.view(b, n_tubes, c, tube_length, cell_size, cell_size)
+
+
+def tube_validity_mask(
+    mask: Tensor,
+    grid_size: int,
+    tube_length: int,
+    temporal_stride: int,
+) -> Tensor:
+    """Derive ``(B, N_tubes)`` boolean validity mask from a ``(B, T)`` frame mask.
+
+    A tube is valid iff at least one of its constituent frames is real.
+    Cells inside the same temporal window share validity (purely temporal
+    derivation).
+    """
+    b, t = mask.shape
+    n_windows = n_temporal_windows(t, tube_length, temporal_stride)
+    # (B, n_windows, tube_length)
+    win_mask = mask.unfold(1, tube_length, temporal_stride)
+    per_window = win_mask.any(dim=-1)  # (B, n_windows)
+    # Broadcast to all spatial cells in the window: (B, n_windows, grid_size**2)
+    per_tube = per_window.unsqueeze(-1).expand(b, n_windows, grid_size * grid_size)
+    return per_tube.reshape(b, n_windows * grid_size * grid_size).contiguous()
+
+
+class _SelfAttnBlock(nn.Module):
+    """Pre-norm self-attention + FFN block (batch_first)."""
+
+    def __init__(
+        self,
+        embed_dim: int,
+        num_heads: int,
+        ffn_dim: int,
+        dropout: float,
+    ) -> None:
+        super().__init__()
+        self.norm1 = nn.LayerNorm(embed_dim)
+        self.attn = nn.MultiheadAttention(
+            embed_dim=embed_dim,
+            num_heads=num_heads,
+            dropout=dropout,
+            batch_first=True,
+        )
+        self.norm2 = nn.LayerNorm(embed_dim)
+        self.ffn = nn.Sequential(
+            nn.Linear(embed_dim, ffn_dim),
+            nn.GELU(),
+            nn.Dropout(dropout),
+            nn.Linear(ffn_dim, embed_dim),
+            nn.Dropout(dropout),
+        )
+
+    def forward(self, x: Tensor) -> Tensor:
+        h = self.norm1(x)
+        attn_out, _ = self.attn(h, h, h, need_weights=False)
+        x = x + attn_out
+        x = x + self.ffn(self.norm2(x))
+        return x
+
+
+class LocalTubeEncoder(nn.Module):
+    """Encode each ``(3, T_t, h, w)`` tube into one embedding via a video transformer.
+
+    Architecture per tube:
+        - Tubelet patch embed: ``Conv3d`` with stride matching its kernel,
+          producing a grid of spatio-temporal tokens.
+        - Prepend a learnable ``[CLS]`` token; add learned positional embeddings.
+        - ``num_layers`` self-attention blocks.
+        - Return the final ``[CLS]`` state as the tube embedding.
+    """
+
+    def __init__(
+        self,
+        tube_length: int,
+        cell_size: int,
+        t_kernel: int,
+        h_patch: int,
+        w_patch: int,
+        embed_dim: int,
+        num_layers: int,
+        num_heads: int,
+        ffn_dim: int,
+        dropout: float,
+    ) -> None:
+        super().__init__()
+        if tube_length % t_kernel != 0:
+            raise ValueError(
+                f"tube_length ({tube_length}) must be divisible "
+                f"by t_kernel ({t_kernel})"
+            )
+        if cell_size % h_patch != 0 or cell_size % w_patch != 0:
+            raise ValueError(
+                f"cell_size ({cell_size}) must be divisible by h_patch ({h_patch}) "
+                f"and w_patch ({w_patch})"
+            )
+        self.tubelet_embed = nn.Conv3d(
+            in_channels=3,
+            out_channels=embed_dim,
+            kernel_size=(t_kernel, h_patch, w_patch),
+            stride=(t_kernel, h_patch, w_patch),
+        )
+        n_t = tube_length // t_kernel
+        n_h = cell_size // h_patch
+        n_w = cell_size // w_patch
+        self.n_tokens = n_t * n_h * n_w
+        self.cls_token = nn.Parameter(torch.zeros(1, 1, embed_dim))
+        nn.init.trunc_normal_(self.cls_token, std=0.02)
+        self.pos_embed = nn.Parameter(torch.zeros(1, self.n_tokens + 1, embed_dim))
+        nn.init.trunc_normal_(self.pos_embed, std=0.02)
+        self.blocks = nn.ModuleList(
+            [
+                _SelfAttnBlock(embed_dim, num_heads, ffn_dim, dropout)
+                for _ in range(num_layers)
+            ]
+        )
+        self.norm = nn.LayerNorm(embed_dim)
+        self.embed_dim = embed_dim
+
+    def forward(self, tubes: Tensor) -> Tensor:
+        """``tubes: (B, N_tubes, 3, T_t, h, w) -> (B, N_tubes, embed_dim)``."""
+        b, n, c, t, h, w = tubes.shape
+        x = tubes.reshape(b * n, c, t, h, w)
+        x = self.tubelet_embed(x)  # (B*N, D, n_t, n_h, n_w)
+        x = x.flatten(2).transpose(1, 2)  # (B*N, n_tokens, D)
+        cls = self.cls_token.expand(x.shape[0], 1, -1)
+        x = torch.cat([cls, x], dim=1)
+        x = x + self.pos_embed
+        for block in self.blocks:
+            x = block(x)
+        cls_out = self.norm(x[:, 0, :])
+        return cls_out.reshape(b, n, self.embed_dim)
+
+
+class _TubeEncoderBase(nn.Module):
+    """Flattens the ``(B, N, …)`` tube axis, calls ``_encode``, reshapes back."""
+
+    embed_dim: int
+
+    def forward(self, tubes: Tensor) -> Tensor:
+        b, n, c, t, h, w = tubes.shape
+        feat = self._encode(tubes.reshape(b * n, c, t, h, w))  # (B*N, D)
+        return feat.reshape(b, n, self.embed_dim)
+
+    def _encode(self, x: Tensor) -> Tensor:  # (B', 3, T, h, w) -> (B', D)
+        raise NotImplementedError
+
+
+class Resnet3DTubeEncoder(_TubeEncoderBase):
+    """Kinetics-400 pretrained r3d_18 applied to each tube."""
+
+    def __init__(
+        self, embed_dim: int, pretrained: bool = True, clip_spatial_size: int = 112
+    ) -> None:
+        super().__init__()
+        self.embed_dim = embed_dim
+        self.clip_spatial_size = clip_spatial_size
+        self.backbone = r3d_18(
+            weights=R3D_18_Weights.KINETICS400_V1 if pretrained else None
+        )
+        feat_dim = self.backbone.fc.in_features
+        self.backbone.fc = nn.Identity()
+        self.proj: nn.Module = (
+            nn.Identity() if embed_dim == feat_dim else nn.Linear(feat_dim, embed_dim)
+        )
+        im_mean, im_std = torch.tensor(_IMAGENET_MEAN), torch.tensor(_IMAGENET_STD)
+        ki_mean, ki_std = torch.tensor(_KINETICS_MEAN), torch.tensor(_KINETICS_STD)
+        self.register_buffer(
+            "_scale", (im_std / ki_std).view(1, 3, 1, 1, 1), persistent=False
+        )
+        self.register_buffer(
+            "_bias",
+            ((im_mean - ki_mean) / ki_std).view(1, 3, 1, 1, 1),
+            persistent=False,
+        )
+
+    def _encode(self, x: Tensor) -> Tensor:
+        x = x * self._scale + self._bias  # ImageNet -> Kinetics
+        s = self.clip_spatial_size
+        if x.shape[-1] != s or x.shape[-2] != s:
+            x = F.interpolate(
+                x, size=(x.shape[2], s, s), mode="trilinear", align_corners=False
+            )
+        return self.proj(self.backbone(x))
+
+
+class ViViTTubeEncoder(_TubeEncoderBase):
+    """ViViT-style tubelet embedding + factorised spatial-then-temporal attention."""
+
+    def __init__(
+        self,
+        tube_length: int,
+        cell_size: int,
+        t_kernel: int,
+        h_patch: int,
+        w_patch: int,
+        embed_dim: int,
+        num_layers: int,
+        num_heads: int,
+        ffn_dim: int,
+        dropout: float,
+    ) -> None:
+        super().__init__()
+        self.embed_dim = embed_dim
+        self.tubelet = nn.Conv3d(
+            3,
+            embed_dim,
+            kernel_size=(t_kernel, h_patch, w_patch),
+            stride=(t_kernel, h_patch, w_patch),
+        )
+        n_t = tube_length // t_kernel
+        n_s = (cell_size // h_patch) * (cell_size // w_patch)
+        self.spatial_pos = nn.Parameter(torch.zeros(1, 1, n_s, embed_dim))
+        self.temporal_pos = nn.Parameter(torch.zeros(1, n_t, 1, embed_dim))
+        nn.init.trunc_normal_(self.spatial_pos, std=0.02)
+        nn.init.trunc_normal_(self.temporal_pos, std=0.02)
+        self.spatial_blocks = nn.ModuleList(
+            [
+                _SelfAttnBlock(embed_dim, num_heads, ffn_dim, dropout)
+                for _ in range(num_layers)
+            ]
+        )
+        self.temporal_blocks = nn.ModuleList(
+            [
+                _SelfAttnBlock(embed_dim, num_heads, ffn_dim, dropout)
+                for _ in range(num_layers)
+            ]
+        )
+        self.norm = nn.LayerNorm(embed_dim)
+
+    def _encode(self, x: Tensor) -> Tensor:
+        z = self.tubelet(x)  # (B', D, T', Hp, Wp)
+        b2, d, tp, hp, wp = z.shape
+        z = z.flatten(3).permute(0, 2, 3, 1)  # (B', T', S, D)
+        z = z + self.spatial_pos + self.temporal_pos
+        s = hp * wp
+        for sblk, tblk in zip(self.spatial_blocks, self.temporal_blocks, strict=True):
+            z = sblk(z.reshape(b2 * tp, s, d)).reshape(b2, tp, s, d)
+            z = (
+                tblk(z.permute(0, 2, 1, 3).reshape(b2 * s, tp, d))
+                .reshape(b2, s, tp, d)
+                .permute(0, 2, 1, 3)
+            )
+        return self.norm(z.reshape(b2, tp * s, d).mean(dim=1))
+
+
+class _ConvLSTMCell(nn.Module):
+    def __init__(self, in_ch: int, hid_ch: int, kernel: int = 3) -> None:
+        super().__init__()
+        self.hid_ch = hid_ch
+        self.conv = nn.Conv2d(in_ch + hid_ch, 4 * hid_ch, kernel, padding=kernel // 2)
+
+    def forward(self, x: Tensor, h: Tensor, c: Tensor) -> tuple[Tensor, Tensor]:
+        i, f, o, g = self.conv(torch.cat([x, h], dim=1)).chunk(4, dim=1)
+        c = torch.sigmoid(f) * c + torch.sigmoid(i) * torch.tanh(g)
+        h = torch.sigmoid(o) * torch.tanh(c)
+        return h, c
+
+
+class ConvLSTMTubeEncoder(_TubeEncoderBase):
+    """Per-frame 2D CNN stem + ConvLSTM recurrence over the tube's frames."""
+
+    def __init__(
+        self, embed_dim: int, base_channels: int = 64, hidden_channels: int = 128
+    ) -> None:
+        super().__init__()
+        self.embed_dim = embed_dim
+        self.hidden_channels = hidden_channels
+        c = base_channels
+        self.stem = nn.Sequential(
+            nn.Conv2d(3, c, 3, stride=2, padding=1),
+            nn.BatchNorm2d(c),
+            nn.GELU(),
+            nn.Conv2d(c, c * 2, 3, stride=2, padding=1),
+            nn.BatchNorm2d(c * 2),
+            nn.GELU(),
+            nn.Conv2d(c * 2, hidden_channels, 3, stride=2, padding=1),
+            nn.BatchNorm2d(hidden_channels),
+            nn.GELU(),
+        )
+        self.cell = _ConvLSTMCell(hidden_channels, hidden_channels)
+        self.proj = nn.Linear(hidden_channels, embed_dim)
+
+    def _encode(self, x: Tensor) -> Tensor:
+        b2, c, t, h, w = x.shape
+        feat = self.stem(x.transpose(1, 2).reshape(b2 * t, c, h, w))
+        _, cc, hh, ww = feat.shape
+        feat = feat.reshape(b2, t, cc, hh, ww)
+        h_t = feat.new_zeros(b2, self.hidden_channels, hh, ww)
+        c_t = feat.new_zeros(b2, self.hidden_channels, hh, ww)
+        for ti in range(t):
+            h_t, c_t = self.cell(feat[:, ti], h_t, c_t)
+        return self.proj(F.adaptive_avg_pool2d(h_t, 1).flatten(1))
+
+
+def _temporal_shift(x: Tensor, n_frames: int, fold_div: int = 4) -> Tensor:
+    """Temporal shift on ``(B'*T, C, H, W)`` (TSM): part of channels shift in time."""
+    bt, c, h, w = x.shape
+    b = bt // n_frames
+    x = x.view(b, n_frames, c, h, w)
+    fold = c // fold_div
+    out = torch.zeros_like(x)
+    out[:, :-1, :fold] = x[:, 1:, :fold]  # shift toward future
+    out[:, 1:, fold : 2 * fold] = x[:, :-1, fold : 2 * fold]  # shift toward past
+    out[:, :, 2 * fold :] = x[:, :, 2 * fold :]  # unchanged
+    return out.view(bt, c, h, w)
+
+
+class _TSMBlock(nn.Module):
+    def __init__(self, in_ch: int, out_ch: int, n_frames: int) -> None:
+        super().__init__()
+        self.n_frames = n_frames
+        self.conv = nn.Sequential(
+            nn.Conv2d(in_ch, out_ch, 3, stride=2, padding=1),
+            nn.BatchNorm2d(out_ch),
+            nn.GELU(),
+        )
+
+    def forward(self, x: Tensor) -> Tensor:
+        return self.conv(_temporal_shift(x, self.n_frames))
+
+
+class TSMTubeEncoder(_TubeEncoderBase):
+    """Temporal Shift Module 2D CNN + temporal mean pooling."""
+
+    def __init__(
+        self, tube_length: int, embed_dim: int, base_channels: int = 64
+    ) -> None:
+        super().__init__()
+        self.embed_dim = embed_dim
+        self.tube_length = tube_length
+        c = base_channels
+        self.blocks = nn.Sequential(
+            _TSMBlock(3, c, tube_length),
+            _TSMBlock(c, c * 2, tube_length),
+            _TSMBlock(c * 2, c * 4, tube_length),
+        )
+        self.proj = nn.Linear(c * 4, embed_dim)
+
+    def _encode(self, x: Tensor) -> Tensor:
+        b2, c, t, h, w = x.shape
+        feat = self.blocks(x.transpose(1, 2).reshape(b2 * t, c, h, w))
+        pooled = (
+            F.adaptive_avg_pool2d(feat, 1).flatten(1).reshape(b2, t, -1).mean(dim=1)
+        )
+        return self.proj(pooled)
+
+
+def build_tube_encoder(
+    kind: str,
+    *,
+    tube_length: int,
+    cell_size: int,
+    t_kernel: int,
+    h_patch: int,
+    w_patch: int,
+    embed_dim: int,
+    num_layers: int,
+    num_heads: int,
+    ffn_dim: int,
+    dropout: float,
+    base_channels: int = 64,
+    resnet_pretrained: bool = True,
+) -> nn.Module:
+    """Construct a per-tube encoder by name (see SPATIAL_COMPARISON.md)."""
+    if kind == "tubelet_transformer":
+        return LocalTubeEncoder(
+            tube_length=tube_length,
+            cell_size=cell_size,
+            t_kernel=t_kernel,
+            h_patch=h_patch,
+            w_patch=w_patch,
+            embed_dim=embed_dim,
+            num_layers=num_layers,
+            num_heads=num_heads,
+            ffn_dim=ffn_dim,
+            dropout=dropout,
+        )
+    if kind == "resnet3d":
+        return Resnet3DTubeEncoder(
+            embed_dim=embed_dim,
+            pretrained=resnet_pretrained,
+            clip_spatial_size=cell_size,
+        )
+    if kind == "vivit":
+        return ViViTTubeEncoder(
+            tube_length=tube_length,
+            cell_size=cell_size,
+            t_kernel=t_kernel,
+            h_patch=h_patch,
+            w_patch=w_patch,
+            embed_dim=embed_dim,
+            num_layers=num_layers,
+            num_heads=num_heads,
+            ffn_dim=ffn_dim,
+            dropout=dropout,
+        )
+    if kind == "convlstm":
+        return ConvLSTMTubeEncoder(embed_dim=embed_dim, base_channels=base_channels)
+    if kind == "tsm":
+        return TSMTubeEncoder(
+            tube_length=tube_length, embed_dim=embed_dim, base_channels=base_channels
+        )
+    raise ValueError(f"unknown tube encoder kind {kind!r}")
+
+
+class LocalBranch(nn.Module):
+    """Full local branch: tube extraction + per-tube encoder + validity mask."""
+
+    def __init__(
+        self,
+        grid_size: int,
+        cell_size: int,
+        tube_length: int,
+        temporal_stride: int,
+        t_kernel: int,
+        h_patch: int,
+        w_patch: int,
+        embed_dim: int,
+        num_layers: int,
+        num_heads: int,
+        ffn_dim: int,
+        dropout: float,
+        encoder_kind: str = "tubelet_transformer",
+    ) -> None:
+        super().__init__()
+        self.grid_size = grid_size
+        self.cell_size = cell_size
+        self.tube_length = tube_length
+        self.temporal_stride = temporal_stride
+        self.encoder = build_tube_encoder(
+            encoder_kind,
+            tube_length=tube_length,
+            cell_size=cell_size,
+            t_kernel=t_kernel,
+            h_patch=h_patch,
+            w_patch=w_patch,
+            embed_dim=embed_dim,
+            num_layers=num_layers,
+            num_heads=num_heads,
+            ffn_dim=ffn_dim,
+            dropout=dropout,
+        )
+        self.embed_dim = embed_dim
+
+    def forward(self, patches: Tensor, mask: Tensor) -> tuple[Tensor, Tensor]:
+        tubes = extract_tubes(
+            patches,
+            grid_size=self.grid_size,
+            cell_size=self.cell_size,
+            tube_length=self.tube_length,
+            temporal_stride=self.temporal_stride,
+        )
+        tube_feats = self.encoder(tubes)
+        tube_mask = tube_validity_mask(
+            mask,
+            grid_size=self.grid_size,
+            tube_length=self.tube_length,
+            temporal_stride=self.temporal_stride,
+        )
+        return tube_feats, tube_mask
diff --git a/experiments/temporal-models/tube-multiscale-fusion/src/tube_multiscale_fusion/model.py b/experiments/temporal-models/tube-multiscale-fusion/src/tube_multiscale_fusion/model.py
new file mode 100644
index 00000000..019eaf2c
--- /dev/null
+++ b/experiments/temporal-models/tube-multiscale-fusion/src/tube_multiscale_fusion/model.py
@@ -0,0 +1,269 @@
+"""TemporalModel implementation for tube-multiscale-fusion.
+
+Wires the YOLO companion + tube building + patch cropping + the trained
+two-branch classifier into the pyrocore :class:`TemporalModel` contract, so
+the model is comparable on the shared leaderboard alongside the other
+temporal models.
+
+The detect -> build_tubes -> filter/interpolate -> crop -> score -> trigger
+pipeline is reused verbatim from ``bbox_tube_temporal`` (the shared
+``bbox-tube-temporal-core`` library); only the classifier differs: here it is
+the two-branch :class:`LitTubeMultiscaleClassifier` instead of the
+single-branch ``TemporalSmokeClassifier``. Both expose the same callable
+contract ``(patches[N, T, 3, H, W], mask[N, T]) -> logits[N]``.
+"""
+
+from __future__ import annotations
+
+from pathlib import Path
+from typing import Any, Self
+
+import torch
+from bbox_tube_temporal.inference import (
+    crop_tube_patches,
+    filter_and_interpolate_tubes,
+    find_first_crossing_trigger,
+    pad_frames_symmetrically,
+    pad_frames_uniform,
+    run_yolo_on_frames,
+    score_tubes,
+)
+from bbox_tube_temporal.tubes import build_tubes
+from pyrocore import Frame, TemporalModel, TemporalModelOutput
+
+from .package import ModelPackage, load_model_package
+
+_PAD_STRATEGIES = {
+    "symmetric": pad_frames_symmetrically,
+    "uniform": pad_frames_uniform,
+}
+
+
+def _select_device(device: str | torch.device | None) -> torch.device:
+    """Resolve the requested device, auto-picking the best available when None.
+
+    Preference order: CUDA > MPS (Apple Silicon) > CPU.
+    """
+    if device is not None:
+        return torch.device(device)
+    if torch.cuda.is_available():
+        return torch.device("cuda")
+    if torch.backends.mps.is_available():
+        return torch.device("mps")
+    return torch.device("cpu")
+
+
+class TubeMultiscaleFusionModel(TemporalModel):
+    """YOLO companion + two-branch tube/global classifier.
+
+    Mirrors :class:`bbox_tube_temporal.model.BboxTubeTemporalModel`; see that
+    module and ``docs/specs/2026-04-15-temporal-model-protocol-design.md`` for
+    the full pipeline description.
+    """
+
+    def __init__(
+        self,
+        *,
+        yolo_model: Any,
+        classifier: Any,
+        config: dict[str, Any],
+        device: str | torch.device | None = None,
+    ) -> None:
+        self._yolo = yolo_model
+        self._device = _select_device(device)
+        self._classifier = classifier.to(self._device).eval()
+        self._cfg = config
+
+    @property
+    def device(self) -> torch.device:
+        return self._device
+
+    @classmethod
+    def from_package(
+        cls,
+        package_path: Path,
+        *,
+        device: str | torch.device | None = None,
+    ) -> Self:
+        pkg: ModelPackage = load_model_package(package_path)
+        return cls(
+            yolo_model=pkg.yolo_model,
+            classifier=pkg.classifier,
+            config=pkg.config,
+            device=device,
+        )
+
+    @classmethod
+    def from_archive(
+        cls,
+        archive_path: Path,
+        *,
+        device: str | torch.device | None = None,
+    ) -> Self:
+        """Alias for :meth:`from_package` (generic name used by eval drivers)."""
+        return cls.from_package(archive_path, device=device)
+
+    def predict(self, frames: list[Frame]) -> TemporalModelOutput:
+        infer = self._cfg["infer"]
+        tubes_cfg = self._cfg["tubes"]
+        mi = self._cfg["model_input"]
+        clf_cfg = self._cfg["classifier"]
+        dec = self._cfg["decision"]
+
+        threshold = float(dec["threshold"])
+        max_frames = int(clf_cfg["max_frames"])
+
+        def _make_details(
+            *,
+            num_frames_input: int,
+            num_truncated: int,
+            padded_indices: list[int],
+            num_candidates: int,
+            kept_tubes: list[dict],
+            trigger_tube_id: int | None,
+        ) -> dict:
+            return {
+                "preprocessing": {
+                    "num_frames_input": num_frames_input,
+                    "num_truncated": num_truncated,
+                    "padded_frame_indices": padded_indices,
+                },
+                "tubes": {
+                    "num_candidates": num_candidates,
+                    "kept": kept_tubes,
+                },
+                "decision": {
+                    "aggregation": "max_logit",
+                    "threshold": threshold,
+                    "trigger_tube_id": trigger_tube_id,
+                },
+            }
+
+        original_len = len(frames)
+        if original_len == 0:
+            return TemporalModelOutput(
+                is_positive=False,
+                trigger_frame_index=None,
+                details=_make_details(
+                    num_frames_input=0,
+                    num_truncated=0,
+                    padded_indices=[],
+                    num_candidates=0,
+                    kept_tubes=[],
+                    trigger_tube_id=None,
+                ),
+            )
+
+        truncated = frames[:max_frames]
+        n_truncated = original_len - len(truncated)
+
+        padded_indices: list[int] = []
+        pad_min = int(infer.get("pad_to_min_frames", 0))
+        if pad_min > 0 and len(truncated) < pad_min:
+            strategy = infer.get("pad_strategy", "symmetric")
+            try:
+                pad_fn = _PAD_STRATEGIES[strategy]
+            except KeyError as e:
+                raise ValueError(
+                    f"unknown pad_strategy {strategy!r}; "
+                    f"expected one of {sorted(_PAD_STRATEGIES)}"
+                ) from e
+            truncated, padded_indices = pad_fn(truncated, min_length=pad_min)
+
+        frame_dets = run_yolo_on_frames(
+            self._yolo,
+            truncated,
+            confidence_threshold=infer["confidence_threshold"],
+            iou_nms=infer["iou_nms"],
+            image_size=infer["image_size"],
+            device=self._device,
+        )
+
+        candidate_tubes = build_tubes(
+            frame_dets,
+            iou_threshold=tubes_cfg["iou_threshold"],
+            max_misses=tubes_cfg["max_misses"],
+        )
+        kept = filter_and_interpolate_tubes(
+            candidate_tubes,
+            min_tube_length=tubes_cfg["infer_min_tube_length"],
+            min_detected_entries=tubes_cfg["min_detected_entries"],
+            interpolate_gaps=tubes_cfg["interpolate_gaps"],
+        )
+
+        if not kept:
+            return TemporalModelOutput(
+                is_positive=False,
+                trigger_frame_index=None,
+                details=_make_details(
+                    num_frames_input=original_len,
+                    num_truncated=n_truncated,
+                    padded_indices=padded_indices,
+                    num_candidates=len(candidate_tubes),
+                    kept_tubes=[],
+                    trigger_tube_id=None,
+                ),
+            )
+
+        patches_per_tube: list[torch.Tensor] = []
+        masks_per_tube: list[torch.Tensor] = []
+        for t in kept:
+            p, m = crop_tube_patches(
+                t,
+                truncated,
+                context_factor=mi["context_factor"],
+                patch_size=mi["patch_size"],
+                max_frames=max_frames,
+                normalization_mean=mi["normalization"]["mean"],
+                normalization_std=mi["normalization"]["std"],
+            )
+            patches_per_tube.append(p.to(self._device))
+            masks_per_tube.append(m.to(self._device))
+
+        logits = score_tubes(
+            self._classifier,
+            patches_per_tube=patches_per_tube,
+            masks_per_tube=masks_per_tube,
+        )
+
+        is_positive, trigger, trigger_tube_id, per_tube_first_crossing = (
+            find_first_crossing_trigger(
+                classifier=self._classifier,
+                tubes=kept,
+                patches_per_tube=patches_per_tube,
+                masks_per_tube=masks_per_tube,
+                full_logits=logits,
+                aggregation="max_logit",
+                threshold=threshold,
+                min_prefix_length=tubes_cfg["infer_min_tube_length"],
+            )
+        )
+
+        logits_list: list[float] = logits.tolist()
+        kept_tubes: list[dict] = []
+        for tube_idx, tube in enumerate(kept):
+            first_crossing = per_tube_first_crossing.get(tube.tube_id, {}).get(
+                "crossing_frame"
+            )
+            kept_tubes.append(
+                {
+                    "tube_id": tube.tube_id,
+                    "start_frame": tube.start_frame,
+                    "end_frame": tube.end_frame,
+                    "logit": logits_list[tube_idx],
+                    "first_crossing_frame": first_crossing,
+                }
+            )
+
+        return TemporalModelOutput(
+            is_positive=is_positive,
+            trigger_frame_index=trigger,
+            details=_make_details(
+                num_frames_input=original_len,
+                num_truncated=n_truncated,
+                padded_indices=padded_indices,
+                num_candidates=len(candidate_tubes),
+                kept_tubes=kept_tubes,
+                trigger_tube_id=trigger_tube_id,
+            ),
+        )
diff --git a/experiments/temporal-models/tube-multiscale-fusion/src/tube_multiscale_fusion/package.py b/experiments/temporal-models/tube-multiscale-fusion/src/tube_multiscale_fusion/package.py
new file mode 100644
index 00000000..b8c51b20
--- /dev/null
+++ b/experiments/temporal-models/tube-multiscale-fusion/src/tube_multiscale_fusion/package.py
@@ -0,0 +1,186 @@
+"""Model packaging: bundle YOLO weights, classifier checkpoint, and config.
+
+Mirrors ``bbox_tube_temporal.package`` (the sibling experiment) so the
+two-branch model plugs into the same serving / leaderboard path. The archive
+is a standard ``.zip`` containing:
+
+- ``manifest.yaml`` — entry point with format version and file pointers.
+- ``yolo_weights.pt`` — ultralytics YOLO checkpoint for the companion detector.
+- ``classifier.ckpt`` — Lightning checkpoint for ``LitTubeMultiscaleClassifier``.
+- ``config.yaml`` — inference config (infer / tubes / model_input / classifier /
+  decision).
+
+Unlike ``bbox_tube_temporal`` this experiment ships only the ``max_logit``
+decision rule, so there is no bundled logistic calibrator.
+"""
+
+from dataclasses import dataclass
+from pathlib import Path
+from typing import Any
+
+import yaml
+
+from .lit_module import LitTubeMultiscaleClassifier
+
+FORMAT_VERSION = 1
+MANIFEST_FILENAME = "manifest.yaml"
+YOLO_WEIGHTS_FILENAME = "yolo_weights.pt"
+CLASSIFIER_CKPT_FILENAME = "classifier.ckpt"
+CONFIG_FILENAME = "config.yaml"
+DEFAULT_EXTRACT_DIR = Path(".cache/tube_multiscale_fusion_model")
+
+
+@dataclass
+class ModelPackage:
+    """A loaded model package: classifier, YOLO model, and full config."""
+
+    classifier: Any  # LitTubeMultiscaleClassifier; Any avoids an import cycle
+    yolo_model: Any  # ultralytics.YOLO; same reason
+    config: dict[str, Any]
+
+    @property
+    def infer(self) -> dict[str, Any]:
+        return self.config["infer"]
+
+    @property
+    def tubes(self) -> dict[str, Any]:
+        return self.config["tubes"]
+
+    @property
+    def model_input(self) -> dict[str, Any]:
+        return self.config["model_input"]
+
+    @property
+    def classifier_cfg(self) -> dict[str, Any]:
+        return self.config["classifier"]
+
+    @property
+    def decision(self) -> dict[str, Any]:
+        return self.config["decision"]
+
+
+def build_model_package(
+    *,
+    yolo_weights_path: Path,
+    classifier_ckpt_path: Path,
+    config: dict[str, Any],
+    variant: str,
+    output_path: Path,
+) -> Path:
+    """Bundle YOLO weights + classifier checkpoint + config into a .zip archive.
+
+    Args:
+        yolo_weights_path: Path to the ultralytics YOLO ``.pt`` file.
+        classifier_ckpt_path: Path to the Lightning ``.ckpt`` for
+            ``LitTubeMultiscaleClassifier``.
+        config: Full package config dict (see module docstring for schema).
+        variant: Identifier recorded in the manifest (informational).
+        output_path: Destination ``.zip`` path.
+
+    Returns:
+        The resolved ``output_path``.
+
+    Raises:
+        FileNotFoundError: If either input file is missing.
+    """
+    import zipfile  # noqa: PLC0415 — keep the heavy stdlib import local to the writer
+
+    if not yolo_weights_path.exists():
+        raise FileNotFoundError(f"YOLO weights not found: {yolo_weights_path}")
+    if not classifier_ckpt_path.exists():
+        raise FileNotFoundError(
+            f"Classifier checkpoint not found: {classifier_ckpt_path}"
+        )
+
+    manifest = {
+        "format_version": FORMAT_VERSION,
+        "variant": variant,
+        "yolo_weights": YOLO_WEIGHTS_FILENAME,
+        "classifier_checkpoint": CLASSIFIER_CKPT_FILENAME,
+        "config": CONFIG_FILENAME,
+    }
+
+    output_path.parent.mkdir(parents=True, exist_ok=True)
+    with zipfile.ZipFile(output_path, "w", compression=zipfile.ZIP_STORED) as zf:
+        zf.writestr(MANIFEST_FILENAME, yaml.dump(manifest, default_flow_style=False))
+        zf.write(yolo_weights_path, YOLO_WEIGHTS_FILENAME)
+        zf.write(classifier_ckpt_path, CLASSIFIER_CKPT_FILENAME)
+        zf.writestr(CONFIG_FILENAME, yaml.dump(config, default_flow_style=False))
+    return output_path.resolve()
+
+
+def _load_yolo(weights_path: Path) -> Any:
+    """Thin wrapper around ultralytics.YOLO.
+
+    The ``ultralytics`` import is deliberately inside the function body so
+    tests can patch ``_load_yolo`` without triggering the heavy import chain
+    (mirrors ``bbox_tube_temporal.package._load_yolo``). ``ultralytics`` is
+    pulled in transitively via ``bbox-tube-temporal-core``.
+    """
+    from ultralytics import YOLO  # noqa: PLC0415
+
+    return YOLO(str(weights_path))
+
+
+def _load_classifier(ckpt_path: Path) -> LitTubeMultiscaleClassifier:
+    """Load a ``LitTubeMultiscaleClassifier`` from a Lightning checkpoint.
+
+    ``pretrained=False`` is forced so timm does not re-download backbone
+    weights — the trained weights already live in the checkpoint.
+    """
+    model = LitTubeMultiscaleClassifier.load_from_checkpoint(
+        str(ckpt_path),
+        map_location="cpu",
+        pretrained=False,
+    )
+    model.eval()
+    return model
+
+
+def load_model_package(
+    package_path: Path,
+    extract_dir: Path = DEFAULT_EXTRACT_DIR,
+) -> ModelPackage:
+    """Load a packaged model archive.
+
+    Args:
+        package_path: Path to a ``.zip`` built by :func:`build_model_package`.
+        extract_dir: Where to extract YOLO weights and classifier ckpt.
+
+    Raises:
+        FileNotFoundError: if ``package_path`` does not exist.
+        KeyError: if the archive is missing expected entries.
+        ValueError: if ``format_version`` is unsupported.
+    """
+    import zipfile  # noqa: PLC0415
+
+    if not package_path.exists():
+        raise FileNotFoundError(f"Archive not found: {package_path}")
+
+    with zipfile.ZipFile(package_path, "r") as zf:
+        names = zf.namelist()
+        if MANIFEST_FILENAME not in names:
+            raise KeyError(f"Archive missing {MANIFEST_FILENAME}")
+        manifest = yaml.safe_load(zf.read(MANIFEST_FILENAME))
+
+        version = manifest.get("format_version")
+        if version != FORMAT_VERSION:
+            raise ValueError(
+                f"Unsupported format_version {version} (expected {FORMAT_VERSION})"
+            )
+
+        yolo_name = manifest["yolo_weights"]
+        ckpt_name = manifest["classifier_checkpoint"]
+        config_name = manifest["config"]
+        for n in (yolo_name, ckpt_name, config_name):
+            if n not in names:
+                raise KeyError(f"Archive missing {n}")
+
+        extract_dir.mkdir(parents=True, exist_ok=True)
+        zf.extract(yolo_name, extract_dir)
+        zf.extract(ckpt_name, extract_dir)
+        config = yaml.safe_load(zf.read(config_name))
+
+    yolo_model = _load_yolo(extract_dir / yolo_name)
+    classifier = _load_classifier(extract_dir / ckpt_name)
+    return ModelPackage(classifier=classifier, yolo_model=yolo_model, config=config)
diff --git a/experiments/temporal-models/tube-multiscale-fusion/tests/__init__.py b/experiments/temporal-models/tube-multiscale-fusion/tests/__init__.py
new file mode 100644
index 00000000..e69de29b
diff --git a/experiments/temporal-models/tube-multiscale-fusion/tests/test_classifier.py b/experiments/temporal-models/tube-multiscale-fusion/tests/test_classifier.py
new file mode 100644
index 00000000..da10fbb9
--- /dev/null
+++ b/experiments/temporal-models/tube-multiscale-fusion/tests/test_classifier.py
@@ -0,0 +1,134 @@
+"""End-to-end tests for TubeMultiscaleClassifier.
+
+These tests substitute a tiny backbone via monkey-patching when possible to
+avoid downloading large DINOv2 weights. The full DINOv2 path lives in
+``test_global_branch.py`` behind ``@pytest.mark.slow``.
+"""
+
+from __future__ import annotations
+
+import pytest
+import torch
+from torch import Tensor, nn
+
+from tube_multiscale_fusion import classifier as classifier_mod
+from tube_multiscale_fusion.classifier import TubeMultiscaleClassifier
+
+
+class _TinyBackbone(nn.Module):
+    """Drop-in replacement for TimmBackbone with the same interface."""
+
+    def __init__(
+        self,
+        name: str,
+        pretrained: bool = True,
+        finetune: bool = False,
+        finetune_last_n_blocks: int = 0,
+        global_pool: str = "token",
+        img_size: int | None = 224,
+    ) -> None:
+        super().__init__()
+        self.feat_dim = 32
+        self.finetune = finetune
+        self.name = name
+        self.conv = nn.Conv2d(3, self.feat_dim, kernel_size=img_size or 224)
+
+    def forward(self, x: Tensor) -> Tensor:
+        return self.conv(x).flatten(2).mean(dim=-1)
+
+
+@pytest.fixture
+def tiny_backbone(monkeypatch: pytest.MonkeyPatch) -> None:
+    """Swap TimmBackbone for a tiny conv-based stand-in for speed."""
+    monkeypatch.setattr(
+        "tube_multiscale_fusion.global_branch.TimmBackbone", _TinyBackbone
+    )
+
+
+def _build_classifier() -> TubeMultiscaleClassifier:
+    return TubeMultiscaleClassifier(
+        backbone="tiny",
+        finetune=False,
+        finetune_last_n_blocks=0,
+        max_frames=16,
+        global_aggregator_num_layers=1,
+        global_aggregator_num_heads=4,
+        global_aggregator_ffn_dim=64,
+        global_aggregator_dropout=0.0,
+        grid_size=4,
+        cell_size=56,
+        tube_length=4,
+        temporal_stride=2,
+        local_t_kernel=2,
+        local_h_patch=14,
+        local_w_patch=14,
+        local_embed_dim=48,
+        local_num_layers=1,
+        local_num_heads=4,
+        local_ffn_dim=96,
+        local_dropout=0.0,
+        d_fusion=64,
+        fusion_num_layers=1,
+        fusion_num_heads=4,
+        fusion_ffn_dim=128,
+        fusion_dropout=0.0,
+        head_hidden_dim=32,
+        head_dropout=0.0,
+        img_size=224,
+        pretrained=False,
+    )
+
+
+def test_classifier_forward_shape(tiny_backbone: None) -> None:
+    del tiny_backbone
+    model = _build_classifier()
+    patches = torch.randn(2, 16, 3, 224, 224)
+    mask = torch.ones(2, 16, dtype=torch.bool)
+    logits = model(patches, mask)
+    assert logits.shape == (2,)
+
+
+def test_classifier_handles_padding(tiny_backbone: None) -> None:
+    del tiny_backbone
+    model = _build_classifier()
+    patches = torch.randn(1, 16, 3, 224, 224)
+    mask = torch.zeros(1, 16, dtype=torch.bool)
+    mask[:, :8] = True
+    logits = model(patches, mask)
+    assert logits.shape == (1,)
+    assert torch.isfinite(logits).all()
+
+
+def test_classifier_gradient_flows_through_both_branches(
+    tiny_backbone: None,
+) -> None:
+    del tiny_backbone
+    model = _build_classifier()
+    patches = torch.randn(1, 16, 3, 224, 224, requires_grad=False)
+    mask = torch.ones(1, 16, dtype=torch.bool)
+    target = torch.tensor([1.0])
+    logits = model(patches, mask)
+    loss = torch.nn.functional.binary_cross_entropy_with_logits(logits, target)
+    loss.backward()
+
+    # Local branch encoder params must have grads.
+    encoder_grads = [
+        p.grad for p in model.local_branch.encoder.parameters() if p.requires_grad
+    ]
+    assert encoder_grads and all(g is not None for g in encoder_grads)
+
+    # Fusion params must have grads.
+    fusion_grads = [p.grad for p in model.fusion.parameters() if p.requires_grad]
+    assert fusion_grads and all(g is not None for g in fusion_grads)
+
+    # Global aggregator params must have grads.
+    agg_grads = [
+        p.grad for p in model.global_branch.aggregator.parameters() if p.requires_grad
+    ]
+    assert agg_grads and all(g is not None for g in agg_grads)
+
+
+def test_classifier_module_referenced(tiny_backbone: None) -> None:
+    """Sanity: the classifier module is what we expect."""
+    del tiny_backbone
+    assert hasattr(classifier_mod, "TubeMultiscaleClassifier")
diff --git a/experiments/temporal-models/tube-multiscale-fusion/tests/test_fusion.py b/experiments/temporal-models/tube-multiscale-fusion/tests/test_fusion.py
new file mode 100644
index 00000000..648b98cd
--- /dev/null
+++ b/experiments/temporal-models/tube-multiscale-fusion/tests/test_fusion.py
@@ -0,0 +1,68 @@
+"""Unit tests for FusionModule."""
+
+from __future__ import annotations
+
+import torch
+
+from tube_multiscale_fusion.fusion import FusionModule
+
+
+def _build(d_fusion: int = 32) -> FusionModule:
+    return FusionModule(
+        global_dim=64,
+        local_dim=48,
+        d_fusion=d_fusion,
+        num_layers=2,
+        num_heads=4,
+        ffn_dim=64,
+        dropout=0.0,
+    )
+
+
+def test_fusion_output_shape() -> None:
+    fusion = _build()
+    g = torch.randn(2, 64)
+    locals_ = torch.randn(2, 7, 48)
+    mask = torch.ones(2, 7, dtype=torch.bool)
+    out = fusion(g, locals_, mask)
+    assert out.shape == (2, 32)
+
+
+def test_fusion_handles_all_masked_row_without_nan() -> None:
+    fusion = _build()
+    g = torch.randn(2, 64)
+    locals_ = torch.randn(2, 7, 48)
+    mask = torch.ones(2, 7, dtype=torch.bool)
+    mask[1] = False  # second sample has no valid locals.
+    fusion.eval()
+    with torch.no_grad():
+        out = fusion(g, locals_, mask)
+    assert torch.isfinite(out).all()
+
+
+def test_fusion_masking_changes_output() -> None:
+    fusion = _build()
+    g = torch.randn(1, 64)
+    locals_ = torch.randn(1, 8, 48)
+    full = torch.ones(1, 8, dtype=torch.bool)
+    half = torch.zeros(1, 8, dtype=torch.bool)
+    half[:, :4] = True
+    fusion.eval()
+    with torch.no_grad():
+        out_full = fusion(g, locals_, full)
+        out_half = fusion(g, locals_, half)
+    assert not torch.allclose(out_full, out_half)
+
+
+def test_fusion_identity_projection_when_dims_match() -> None:
+    fusion = FusionModule(
+        global_dim=32,
+        local_dim=32,
+        d_fusion=32,
+        num_layers=1,
+        num_heads=4,
+        ffn_dim=64,
+        dropout=0.0,
+    )
+    assert isinstance(fusion.global_proj, torch.nn.Identity)
+    assert isinstance(fusion.local_proj, torch.nn.Identity)
diff --git a/experiments/temporal-models/tube-multiscale-fusion/tests/test_global_branch.py b/experiments/temporal-models/tube-multiscale-fusion/tests/test_global_branch.py
new file mode 100644
index 00000000..e00121a5
--- /dev/null
+++ b/experiments/temporal-models/tube-multiscale-fusion/tests/test_global_branch.py
@@ -0,0 +1,106 @@
+"""Unit tests for the global branch: TimmBackbone wrapper + GlobalAggregator."""
+
+from __future__ import annotations
+
+import pytest
+import torch
+
+from tube_multiscale_fusion.global_branch import (
+    GlobalAggregator,
+    GlobalBranch,
+)
+
+
+def test_global_aggregator_shape() -> None:
+    agg = GlobalAggregator(
+        feat_dim=64,
+        num_layers=2,
+        num_heads=4,
+        ffn_dim=128,
+        dropout=0.0,
+        max_frames=16,
+    )
+    feats = torch.randn(2, 16, 64)
+    mask = torch.ones(2, 16, dtype=torch.bool)
+    out = agg(feats, mask)
+    assert out.shape == (2, 64)
+
+
+def test_global_aggregator_masking_changes_output() -> None:
+    """Masking out frames should change the [CLS] readout."""
+    agg = GlobalAggregator(
+        feat_dim=32,
+        num_layers=1,
+        num_heads=4,
+        ffn_dim=64,
+        dropout=0.0,
+        max_frames=8,
+    )
+    feats = torch.randn(1, 8, 32)
+    full_mask = torch.ones(1, 8, dtype=torch.bool)
+    partial = torch.zeros(1, 8, dtype=torch.bool)
+    partial[:, :4] = True
+    with torch.no_grad():
+        out_full = agg(feats, full_mask)
+        out_partial = agg(feats, partial)
+    assert not torch.allclose(out_full, out_partial)
+
+
+def test_global_aggregator_rejects_too_many_frames() -> None:
+    agg = GlobalAggregator(
+        feat_dim=32,
+        num_layers=1,
+        num_heads=4,
+        ffn_dim=64,
+        dropout=0.0,
+        max_frames=8,
+    )
+    feats = torch.randn(1, 16, 32)
+    mask = torch.ones(1, 16, dtype=torch.bool)
+    with pytest.raises(ValueError, match="max_frames"):
+        agg(feats, mask)
+
+
+@pytest.mark.slow
+def test_global_branch_with_dinov2_frozen() -> None:
+    """Smoke test the full branch with the real DINOv2 backbone (downloads weights)."""
+    branch = GlobalBranch(
+        backbone="vit_small_patch14_dinov2.lvd142m",
+        finetune=False,
+        finetune_last_n_blocks=0,
+        max_frames=16,
+        aggregator_num_layers=1,
+        aggregator_num_heads=6,
+        aggregator_ffn_dim=384,
+        aggregator_dropout=0.0,
+    )
+    assert branch.feat_dim == 384
+    # All backbone params should be frozen.
+    for p in branch.backbone.backbone.parameters():
+        assert not p.requires_grad
+    patches = torch.randn(1, 16, 3, 224, 224)
+    mask = torch.ones(1, 16, dtype=torch.bool)
+    branch.eval()
+    with torch.no_grad():
+        out = branch(patches, mask)
+    assert out.shape == (1, 384)
+
+
+@pytest.mark.slow
+def test_global_branch_finetune_last_block_unfrozen() -> None:
+    branch = GlobalBranch(
+        backbone="vit_small_patch14_dinov2.lvd142m",
+        finetune=True,
+        finetune_last_n_blocks=1,
+        max_frames=16,
+        aggregator_num_layers=1,
+        aggregator_num_heads=6,
+        aggregator_ffn_dim=384,
+        aggregator_dropout=0.0,
+    )
+    blocks = list(branch.backbone.backbone.blocks)
+    # Last block trainable, second-to-last block frozen.
+    assert all(p.requires_grad for p in blocks[-1].parameters())
+    assert all(not p.requires_grad for p in blocks[-2].parameters())
+    # Aggregator always trainable.
+    assert all(p.requires_grad for p in branch.aggregator.parameters())
diff --git a/experiments/temporal-models/tube-multiscale-fusion/tests/test_local_branch.py b/experiments/temporal-models/tube-multiscale-fusion/tests/test_local_branch.py
new file mode 100644
index 00000000..441c101d
--- /dev/null
+++ b/experiments/temporal-models/tube-multiscale-fusion/tests/test_local_branch.py
@@ -0,0 +1,169 @@
+"""Unit tests for the local branch: tube extraction + encoder."""
+
+from __future__ import annotations
+
+import pytest
+import torch
+
+from tube_multiscale_fusion.local_branch import (
+    LocalBranch,
+    LocalTubeEncoder,
+    extract_tubes,
+    n_temporal_windows,
+    tube_validity_mask,
+)
+
+
+def test_n_temporal_windows_stride2() -> None:
+    assert n_temporal_windows(16, 4, 2) == 7
+
+
+def test_n_temporal_windows_stride4_no_overlap() -> None:
+    assert n_temporal_windows(16, 4, 4) == 4
+
+
+def test_extract_tubes_shape_default() -> None:
+    b, t, c, h, w = 2, 16, 3, 224, 224
+    patches = torch.randn(b, t, c, h, w)
+    tubes = extract_tubes(
+        patches, grid_size=4, cell_size=56, tube_length=4, temporal_stride=2
+    )
+    # n_windows=7, n_cells=16 -> 112 tubes
+    assert tubes.shape == (b, 7 * 16, c, 4, 56, 56)
+
+
+def test_extract_tubes_shape_no_overlap() -> None:
+    patches = torch.randn(1, 16, 3, 224, 224)
+    tubes = extract_tubes(
+        patches, grid_size=4, cell_size=56, tube_length=4, temporal_stride=4
+    )
+    assert tubes.shape == (1, 4 * 16, 3, 4, 56, 56)
+
+
+def test_extract_tubes_content_first_cell_first_window() -> None:
+    """Tube (window=0, cell=(0,0)) must equal patches[:, 0:4, :, :56, :56]."""
+    patches = torch.randn(1, 16, 3, 224, 224)
+    tubes = extract_tubes(
+        patches, grid_size=4, cell_size=56, tube_length=4, temporal_stride=2
+    )
+    expected = patches[:, 0:4, :, :56, :56].permute(0, 2, 1, 3, 4)  # (B, C, T, H, W)
+    # Tube index 0 = window 0, cell (0, 0).
+    assert torch.allclose(tubes[:, 0], expected)
+
+
+def test_extract_tubes_content_second_window_first_cell() -> None:
+    """Window 1 with stride=2 covers frames 2..5."""
+    patches = torch.randn(1, 16, 3, 224, 224)
+    tubes = extract_tubes(
+        patches, grid_size=4, cell_size=56, tube_length=4, temporal_stride=2
+    )
+    # n_cells_per_window = 16, so window 1 starts at tube index 16.
+    expected = patches[:, 2:6, :, :56, :56].permute(0, 2, 1, 3, 4)
+    assert torch.allclose(tubes[:, 16], expected)
+
+
+def test_extract_tubes_rejects_mismatched_resolution() -> None:
+    patches = torch.randn(1, 16, 3, 224, 224)
+    with pytest.raises(ValueError, match="grid_size"):
+        extract_tubes(
+            patches, grid_size=3, cell_size=56, tube_length=4, temporal_stride=2
+        )
+
+
+def test_tube_validity_mask_all_valid() -> None:
+    mask = torch.ones(2, 16, dtype=torch.bool)
+    tube_mask = tube_validity_mask(mask, grid_size=4, tube_length=4, temporal_stride=2)
+    assert tube_mask.shape == (2, 7 * 16)
+    assert tube_mask.all()
+
+
+def test_tube_validity_mask_partial_padding() -> None:
+    """Frames 8..15 padded -> windows covering only padded frames must be invalid."""
+    mask = torch.zeros(1, 16, dtype=torch.bool)
+    mask[:, :8] = True
+    tube_mask = tube_validity_mask(mask, grid_size=4, tube_length=4, temporal_stride=2)
+    # Windows: [0:4], [2:6], [4:8], [6:10], [8:12], [10:14], [12:16]
+    # Valid windows: 0..3 (touch frames < 8); invalid: 4..6 (only padded).
+    per_window = tube_mask.reshape(1, 7, 16).any(dim=-1)
+    assert per_window[0].tolist() == [True, True, True, True, False, False, False]
+
+
+def test_tube_validity_mask_zero_valid_frames() -> None:
+    mask = torch.zeros(1, 16, dtype=torch.bool)
+    tube_mask = tube_validity_mask(mask, grid_size=4, tube_length=4, temporal_stride=2)
+    assert tube_mask.shape == (1, 112)
+    assert not tube_mask.any()
+
+
+def test_local_tube_encoder_shape() -> None:
+    encoder = LocalTubeEncoder(
+        tube_length=4,
+        cell_size=56,
+        t_kernel=2,
+        h_patch=14,
+        w_patch=14,
+        embed_dim=128,
+        num_layers=2,
+        num_heads=4,
+        ffn_dim=256,
+        dropout=0.0,
+    )
+    tubes = torch.randn(2, 5, 3, 4, 56, 56)
+    out = encoder(tubes)
+    assert out.shape == (2, 5, 128)
+
+
+def test_local_tube_encoder_token_count() -> None:
+    """Tubelet conv 2x14x14 over 4x56x56 -> 2 * 4 * 4 = 32 tokens (+1 CLS)."""
+    encoder = LocalTubeEncoder(
+        tube_length=4,
+        cell_size=56,
+        t_kernel=2,
+        h_patch=14,
+        w_patch=14,
+        embed_dim=64,
+        num_layers=1,
+        num_heads=4,
+        ffn_dim=128,
+        dropout=0.0,
+    )
+    assert encoder.n_tokens == 32
+
+
+def test_local_branch_end_to_end_shape_and_mask() -> None:
+    branch = LocalBranch(
+        grid_size=4,
+        cell_size=56,
+        tube_length=4,
+        temporal_stride=2,
+        t_kernel=2,
+        h_patch=14,
+        w_patch=14,
+        embed_dim=64,
+        num_layers=1,
+        num_heads=4,
+        ffn_dim=128,
+        dropout=0.0,
+    )
+    patches = torch.randn(2, 16, 3, 224, 224)
+    mask = torch.ones(2, 16, dtype=torch.bool)
+    tube_feats, tube_mask = branch(patches, mask)
+    assert tube_feats.shape == (2, 112, 64)
+    assert tube_mask.shape == (2, 112)
+    assert tube_mask.all()
+
+
+def test_local_branch_rejects_invalid_tube_length() -> None:
+    with pytest.raises(ValueError, match="tube_length"):
+        LocalTubeEncoder(
+            tube_length=4,
+            cell_size=56,
+            t_kernel=3,  # 4 % 3 != 0
+            h_patch=14,
+            w_patch=14,
+            embed_dim=64,
+            num_layers=1,
+            num_heads=4,
+            ffn_dim=128,
+            dropout=0.0,
+        )
diff --git a/experiments/temporal-models/tube-multiscale-fusion/tests/test_model.py b/experiments/temporal-models/tube-multiscale-fusion/tests/test_model.py
new file mode 100644
index 00000000..f9ee49f7
--- /dev/null
+++ b/experiments/temporal-models/tube-multiscale-fusion/tests/test_model.py
@@ -0,0 +1,170 @@
+"""Tests for TubeMultiscaleFusionModel.predict().
+
+The detect -> tube -> crop -> score -> trigger pipeline is exercised with a
+mocked YOLO and a stub classifier that returns a fixed logit, so the wiring is
+verified deterministically without weights. The real two-branch classifier is
+covered by ``test_classifier.py``; the lib stages by the bbox-tube-temporal
+suite.
+"""
+
+from pathlib import Path
+from unittest.mock import MagicMock
+
+import numpy as np
+import pytest
+import torch
+from PIL import Image
+from pyrocore.types import Frame
+from torch import Tensor, nn
+
+from tube_multiscale_fusion.model import TubeMultiscaleFusionModel
+
+TEST_CONFIG: dict = {
+    "infer": {
+        "confidence_threshold": 0.01,
+        "iou_nms": 0.2,
+        "image_size": 64,
+        "pad_to_min_frames": 0,
+        "pad_strategy": "symmetric",
+    },
+    "tubes": {
+        "iou_threshold": 0.2,
+        "max_misses": 2,
+        "min_tube_length": 4,
+        "infer_min_tube_length": 2,
+        "min_detected_entries": 2,
+        "interpolate_gaps": True,
+    },
+    "model_input": {
+        "context_factor": 1.5,
+        "patch_size": 8,
+        "normalization": {
+            "mean": [0.485, 0.456, 0.406],
+            "std": [0.229, 0.224, 0.225],
+        },
+    },
+    "classifier": {"max_frames": 6},
+    "decision": {"aggregation": "max_logit", "threshold": 0.0},
+}
+
+
+class _StubClassifier(nn.Module):
+    """Returns a constant logit per tube, regardless of input."""
+
+    def __init__(self, logit: float) -> None:
+        super().__init__()
+        self._logit = logit
+
+    def forward(self, patches: Tensor, mask: Tensor) -> Tensor:  # noqa: ARG002
+        return torch.full((patches.shape[0],), self._logit, dtype=torch.float32)
+
+
+def _fake_yolo_factory(
+    per_frame_xywhn: list[list[tuple[float, float, float, float, float]]],
+):
+    """Return a mock YOLO whose ``.predict`` yields fixed detections per frame."""
+
+    def fake_predict(paths: list[str], **_kwargs):
+        assert len(paths) == len(per_frame_xywhn)
+        results = []
+        for boxes in per_frame_xywhn:
+            r = MagicMock()
+            r.boxes = MagicMock()
+            if not boxes:
+                r.boxes.__len__ = lambda self: 0
+                r.boxes.xywhn = torch.zeros(0, 4)
+                r.boxes.conf = torch.zeros(0)
+                r.boxes.cls = torch.zeros(0)
+            else:
+                n = len(boxes)
+                r.boxes.__len__ = lambda self, n=n: n
+                r.boxes.xywhn = torch.tensor(
+                    [[c, cy, w, h] for (c, cy, w, h, _) in boxes]
+                )
+                r.boxes.conf = torch.tensor([conf for (_, _, _, _, conf) in boxes])
+                r.boxes.cls = torch.zeros(n)
+            results.append(r)
+        return results
+
+    m = MagicMock()
+    m.predict.side_effect = fake_predict
+    return m
+
+
+@pytest.fixture()
+def red_frames(tmp_path: Path) -> list[Frame]:
+    frames = []
+    for i in range(6):
+        arr = np.full((64, 64, 3), fill_value=[180, 30, 30], dtype=np.uint8)
+        p = tmp_path / f"f_{i:02d}.jpg"
+        Image.fromarray(arr).save(p, format="JPEG")
+        frames.append(Frame(frame_id=p.stem, image_path=p, timestamp=None))
+    return frames
+
+
+def _model(yolo, logit: float) -> TubeMultiscaleFusionModel:
+    return TubeMultiscaleFusionModel(
+        yolo_model=yolo,
+        classifier=_StubClassifier(logit),
+        config=TEST_CONFIG,
+        device="cpu",
+    )
+
+
+def test_empty_frames_returns_negative() -> None:
+    yolo = MagicMock()
+    out = _model(yolo, logit=10.0).predict(frames=[])
+    assert out.is_positive is False
+    assert out.trigger_frame_index is None
+    assert out.details["preprocessing"]["num_frames_input"] == 0
+    assert out.details["tubes"]["num_candidates"] == 0
+    yolo.predict.assert_not_called()
+
+
+def test_zero_detections_means_negative(red_frames: list[Frame]) -> None:
+    yolo = _fake_yolo_factory([[] for _ in red_frames])
+    out = _model(yolo, logit=10.0).predict(frames=red_frames)
+    assert out.is_positive is False
+    assert out.details["tubes"]["num_candidates"] == 0
+    assert out.details["tubes"]["kept"] == []
+
+
+def test_short_tube_below_infer_floor_discarded(red_frames: list[Frame]) -> None:
+    # Only frame 0 has a detection -> tube length 1, below min_detected_entries=2.
+    per_frame = [[(0.5, 0.5, 0.1, 0.1, 0.9)]] + [[] for _ in red_frames[1:]]
+    yolo = _fake_yolo_factory(per_frame)
+    out = _model(yolo, logit=10.0).predict(frames=red_frames)
+    assert out.is_positive is False
+    assert out.details["tubes"]["num_candidates"] == 1
+    assert out.details["tubes"]["kept"] == []
+
+
+def test_full_tube_positive_fires_with_trigger(red_frames: list[Frame]) -> None:
+    per_frame = [[(0.5, 0.5, 0.1, 0.1, 0.9)] for _ in red_frames]
+    yolo = _fake_yolo_factory(per_frame)
+    out = _model(yolo, logit=10.0).predict(frames=red_frames)
+    assert out.is_positive is True
+    assert out.trigger_frame_index is not None
+    assert out.details["decision"]["trigger_tube_id"] is not None
+    assert len(out.details["tubes"]["kept"]) == 1
+    # logit 10 >= threshold 0 at the shortest scored prefix (min_prefix_length=2)
+    assert out.trigger_frame_index == 1
+
+
+def test_full_tube_negative_when_below_threshold(red_frames: list[Frame]) -> None:
+    per_frame = [[(0.5, 0.5, 0.1, 0.1, 0.9)] for _ in red_frames]
+    yolo = _fake_yolo_factory(per_frame)
+    out = _model(yolo, logit=-10.0).predict(frames=red_frames)
+    assert out.is_positive is False
+    assert out.trigger_frame_index is None
+    assert len(out.details["tubes"]["kept"]) == 1
+
+
+def test_truncation_to_max_frames(red_frames: list[Frame]) -> None:
+    # 6 real + 3 extra = 9; max_frames=6 so YOLO only sees the first 6.
+    extra = red_frames + red_frames[:3]
+    per_frame = [[(0.5, 0.5, 0.1, 0.1, 0.9)] for _ in range(6)]
+    yolo = _fake_yolo_factory(per_frame)
+    out = _model(yolo, logit=10.0).predict(frames=extra)
+    assert out.details["preprocessing"]["num_frames_input"] == 9
+    assert out.details["preprocessing"]["num_truncated"] == 3
diff --git a/experiments/temporal-models/tube-multiscale-fusion/tests/test_smoke.py b/experiments/temporal-models/tube-multiscale-fusion/tests/test_smoke.py
new file mode 100644
index 00000000..cf207b2f
--- /dev/null
+++ b/experiments/temporal-models/tube-multiscale-fusion/tests/test_smoke.py
@@ -0,0 +1,6 @@
+"""Smoke tests to verify the test infrastructure works."""
+
+
+def test_placeholder():
+    """Placeholder test — replace with real tests as the experiment develops."""
+    assert True
diff --git a/experiments/temporal-models/tube-multiscale-fusion/uv.lock b/experiments/temporal-models/tube-multiscale-fusion/uv.lock
new file mode 100644
index 00000000..550da8a6
--- /dev/null
+++ b/experiments/temporal-models/tube-multiscale-fusion/uv.lock
@@ -0,0 +1,5106 @@
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+    "python_full_version >= '3.12' and python_full_version < '3.14' and platform_machine == 'aarch64' and sys_platform == 'linux'",
+    "python_full_version >= '3.14' and sys_platform == 'win32'",
+    "python_full_version >= '3.14' and sys_platform == 'emscripten'",
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