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Arun-George-ZachariahArun-George-Zachariah
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TAO 5.1 Release - PyTorch
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‎docker/requirements-pip-pytorch.txt‎

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@@ -7,5 +7,7 @@ pytorch-lightning==1.8.5
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pytorch_metric_learning==1.7.1
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pytorch-msssim
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thop
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timm==0.6.11
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timm>=0.9.6.dev0
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torchmetrics==0.10.3
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open-clip-torch[training]==2.20.0
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ftfy

‎nvidia_tao_pytorch/core/mmlab/mmclassification/classification_default_config.py‎

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@@ -265,6 +265,8 @@ class BackboneConfig:
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type: str = "fan_tiny_8_p4_hybrid"
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custom_args: Optional[Dict[Any, Any]] = None
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freeze: bool = False
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pretrained: Optional[str] = None
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@dataclass

‎nvidia_tao_pytorch/cv/classification/experiment_specs/export_clip_imagenet.yaml‎

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results_dir: ???
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export:
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checkpoint: "???"
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onnx_file: "???"
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verify: true
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dataset:
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data:
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samples_per_gpu: 512
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workers_per_gpu: 8
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train:
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data_prefix: ???
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pipeline: # Augmentations alone
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- type: RandomResizedCrop
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size: 224
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- type: RandomFlip
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flip_prob: 0.5
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direction: "horizontal"
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- type: ColorJitter
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brightness: 0.4
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contrast: 0.4
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saturation: 0.4
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val:
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data_prefix: ???
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test:
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data_prefix: ???
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model:
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backbone:
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type: "open_clip"
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custom_args:
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model_name: "ViT-B-32"
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freeze: true
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pretrained: ???
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init_cfg:
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checkpoint: ???
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head:
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type: LinearClsHead
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num_classes: 1000
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in_channels: 512
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loss:
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type: CrossEntropyLoss
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loss_weight: 1.0
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use_soft: False
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topk: [1, 5]
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train:
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train_config:
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find_unused_parameters: True
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optimizer:
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type: AdamW
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lr: 1e-3
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lr_config:
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policy: CosineAnnealing
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min_lr: 0
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warmup: 'linear'
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warmup_iters: 10
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warmup_by_epoch: False
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optimizer_config:
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grad_clip:
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max_norm: 5.0
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runner:
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max_epochs: 10
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checkpoint_config:
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interval: 1
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logging:
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interval: 50
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validate: True
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evaluation:
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interval: 1

‎nvidia_tao_pytorch/cv/classification/experiment_specs/export_dinov2_imagenet.yaml‎

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results_dir: ???
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export:
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checkpoint: "???"
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onnx_file: "???"
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verify: true
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dataset:
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data:
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samples_per_gpu: 512
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workers_per_gpu: 8
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train:
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data_prefix: ???
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pipeline: # Augmentations alone
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- type: RandomResizedCrop
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size: 224
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- type: RandomFlip
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flip_prob: 0.5
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direction: "horizontal"
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- type: ColorJitter
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brightness: 0.4
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contrast: 0.4
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saturation: 0.4
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val:
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data_prefix: ???
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test:
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data_prefix: ???
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model:
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backbone:
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type: "vit_large_patch14_dinov2_swiglu"
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freeze: true
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pretrained: ???
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init_cfg:
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checkpoint: ???
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head:
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type: LinearClsHead
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num_classes: 1000
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in_channels: 1024
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loss:
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type: CrossEntropyLoss
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loss_weight: 1.0
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use_soft: False
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topk: [1, 5]
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train:
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train_config:
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find_unused_parameters: True
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optimizer:
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type: AdamW
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lr: 1e-3
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lr_config:
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policy: CosineAnnealing
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min_lr: 5e-6
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warmup: 'linear'
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warmup_iters: 10
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warmup_by_epoch: False
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optimizer_config:
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grad_clip:
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max_norm: 5.0
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runner:
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max_epochs: 10
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checkpoint_config:
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interval: 1
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logging:
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interval: 50
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validate: True
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evaluation:
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interval: 1

‎nvidia_tao_pytorch/cv/classification/experiment_specs/inference_clip_imagenet.yaml‎

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results_dir: ???
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inference:
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checkpoint: ???
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dataset:
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data:
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samples_per_gpu: 512
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workers_per_gpu: 8
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train:
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data_prefix: ???
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pipeline: # Augmentations alone
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- type: RandomResizedCrop
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size: 224
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- type: RandomFlip
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flip_prob: 0.5
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direction: "horizontal"
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- type: ColorJitter
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brightness: 0.4
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contrast: 0.4
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saturation: 0.4
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val:
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data_prefix: ???
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test:
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data_prefix: ???
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model:
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backbone:
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type: "open_clip"
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custom_args:
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model_name: "ViT-B-32"
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freeze: true
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pretrained: ???
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init_cfg:
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checkpoint: ???
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head:
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type: LinearClsHead
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num_classes: 1000
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in_channels: 512
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loss:
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type: CrossEntropyLoss
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loss_weight: 1.0
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use_soft: False
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topk: [1, 5]
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train:
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train_config:
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find_unused_parameters: True
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optimizer:
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type: AdamW
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lr: 1e-3
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lr_config:
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policy: CosineAnnealing
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min_lr: 0
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warmup: 'linear'
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warmup_iters: 10
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warmup_by_epoch: False
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optimizer_config:
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grad_clip:
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max_norm: 5.0
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runner:
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max_epochs: 10
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checkpoint_config:
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interval: 1
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logging:
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interval: 50
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validate: True
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evaluation:
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interval: 1

‎nvidia_tao_pytorch/cv/classification/experiment_specs/inference_dinov2_imagenet.yaml‎

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results_dir: ???
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inference:
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checkpoint: ???
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dataset:
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data:
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samples_per_gpu: 512
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workers_per_gpu: 8
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train:
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data_prefix: ???
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pipeline: # Augmentations alone
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- type: RandomResizedCrop
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size: 224
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- type: RandomFlip
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flip_prob: 0.5
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direction: "horizontal"
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- type: ColorJitter
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brightness: 0.4
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contrast: 0.4
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saturation: 0.4
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val:
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data_prefix: ???
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test:
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data_prefix: ???
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model:
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backbone:
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type: "vit_large_patch14_dinov2_swiglu"
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freeze: true
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pretrained: ???
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init_cfg:
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checkpoint: ???
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head:
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type: LinearClsHead
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num_classes: 1000
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in_channels: 1024
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loss:
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type: CrossEntropyLoss
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loss_weight: 1.0
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use_soft: False
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topk: [1, 5]
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train:
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train_config:
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find_unused_parameters: True
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optimizer:
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type: AdamW
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lr: 1e-3
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lr_config:
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policy: CosineAnnealing
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min_lr: 5e-6
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warmup: 'linear'
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warmup_iters: 10
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warmup_by_epoch: False
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optimizer_config:
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grad_clip:
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max_norm: 5.0
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runner:
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max_epochs: 10
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checkpoint_config:
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interval: 1
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logging:
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interval: 50
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validate: True
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evaluation:
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interval: 1

‎nvidia_tao_pytorch/cv/classification/experiment_specs/test_clip_imagenet.yaml‎

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results_dir: ???
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evaluate:
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checkpoint: ???
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dataset:
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data:
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samples_per_gpu: 512
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workers_per_gpu: 8
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train:
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data_prefix: ???
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pipeline: # Augmentations alone
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- type: RandomResizedCrop
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size: 224
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- type: RandomFlip
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flip_prob: 0.5
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direction: "horizontal"
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- type: ColorJitter
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brightness: 0.4
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contrast: 0.4
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saturation: 0.4
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val:
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data_prefix: ???
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test:
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data_prefix: ???
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model:
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backbone:
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type: "open_clip"
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custom_args:
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model_name: "ViT-B-32"
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freeze: true
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pretrained: ???
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init_cfg:
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checkpoint: ???
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head:
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type: LinearClsHead
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num_classes: 1000
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in_channels: 512
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loss:
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type: CrossEntropyLoss
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loss_weight: 1.0
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use_soft: False
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topk: [1, 5]
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train:
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train_config:
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find_unused_parameters: True
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optimizer:
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type: AdamW
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lr: 1e-3
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lr_config:
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policy: CosineAnnealing
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min_lr: 0
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warmup: 'linear'
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warmup_iters: 10
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warmup_by_epoch: False
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optimizer_config:
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grad_clip:
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max_norm: 5.0
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runner:
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max_epochs: 10
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checkpoint_config:
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interval: 1
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logging:
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interval: 50
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validate: True
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evaluation:
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interval: 1

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