|
| 1 | +## Learn from Examples |
| 2 | +Learn the basics of fastdup through interactive examples. View the notebooks on GitHub or nbviewer. Even better, run them on Google Colab or Kaggle, for free. |
| 3 | + |
| 4 | +<table> |
| 5 | + <tr> |
| 6 | + <td rowspan="4" width="160"> |
| 7 | + <a href="https://visual-layer.readme.io/docs/cleaning-image-dataset"> |
| 8 | + <img src="gallery/food_thumbnail.jpg" width="200"> |
| 9 | + </a> |
| 10 | + </td> |
| 11 | + <td rowspan="4"> |
| 12 | + <b>🧹 Clean Image Folder:</b> Learn how to analyze and clean a folder of images from potential issues and export a list of problematic files for further action. If you have an unorganized folder of images, this is a good place to start. |
| 13 | + <br> |
| 14 | + <br> |
| 15 | + <b>📌 Dataset:</b> <a href="https://data.vision.ee.ethz.ch/cvl/datasets_extra/food-101/">Food-101</a>. |
| 16 | + </td> |
| 17 | + <td align="center" width="80"> |
| 18 | + <a href="https://nbviewer.org/github/visual-layer/fastdup/blob/main/examples/cleaning-image-dataset.ipynb"> |
| 19 | + <img src="./gallery/nbviewer_logo.png" height="30"> |
| 20 | + </a> |
| 21 | + </td> |
| 22 | + </tr> |
| 23 | + <tr> |
| 24 | + <td align="center"> |
| 25 | + <a href="https://github.com/visual-layer/fastdup/blob/main/examples/cleaning-image-dataset.ipynb"> |
| 26 | + <img src="./gallery/github_logo.png" height="25"> |
| 27 | + </a> |
| 28 | + </td> |
| 29 | + </tr> |
| 30 | + <tr> |
| 31 | + <td align="center"> |
| 32 | + <a href="https://colab.research.google.com/github/visual-layer/fastdup/blob/main/examples/cleaning-image-dataset.ipynb"> |
| 33 | + <img src="./gallery/colab_logo.png" height="20"> |
| 34 | + </a> |
| 35 | + </td> |
| 36 | + </tr> |
| 37 | + <tr> |
| 38 | + <td align="center"> |
| 39 | + <a href="https://kaggle.com/kernels/welcome?src=https://github.com/visual-layer/fastdup/blob/main/examples/cleaning-image-dataset.ipynb"> |
| 40 | + <img src="./gallery/kaggle_logo.png" height="25"> |
| 41 | + </a> |
| 42 | + </td> |
| 43 | + </tr> |
| 44 | + <!-- ------------------------------------------------------------------- --> |
| 45 | + <tr> |
| 46 | + <td rowspan="4" width="160"> |
| 47 | + <a href="https://visual-layer.readme.io/docs/analyzing-labeled-images"> |
| 48 | + <img src="./gallery/imagenette_thumbnail.jpg" width="200"> |
| 49 | + </a> |
| 50 | + </td> |
| 51 | + <td rowspan="4"> |
| 52 | + <b>🖼 Analyze Image Classification Dataset:</b> Learn how to load a labeled image classification dataset and analyze for potential issues. If you have labeled ImageNet-style folder structure, have a go! |
| 53 | + <br> |
| 54 | + <br> |
| 55 | + <b>📌 Dataset:</b> <a href="https://github.com/fastai/imagenette">Imagenette</a>. |
| 56 | + </td> |
| 57 | + <td align="center" width="80"> |
| 58 | + <a href="https://nbviewer.org/github/visual-layer/fastdup/blob/main/examples/analyzing-image-classification-dataset.ipynb"> |
| 59 | + <img src="./gallery/nbviewer_logo.png" height="30"> |
| 60 | + </a> |
| 61 | + </td> |
| 62 | + </tr> |
| 63 | + <tr> |
| 64 | + <td align="center"> |
| 65 | + <a href="https://github.com/visual-layer/fastdup/blob/main/examples/analyzing-image-classification-dataset.ipynb"> |
| 66 | + <img src="./gallery/github_logo.png" height="25"> |
| 67 | + </a> |
| 68 | + </td> |
| 69 | + </tr> |
| 70 | + <tr> |
| 71 | + <td align="center"> |
| 72 | + <a href="https://colab.research.google.com/github/visual-layer/fastdup/blob/main/examples/analyzing-image-classification-dataset.ipynb"> |
| 73 | + <img src="./gallery/colab_logo.png" height="20"> |
| 74 | + </a> |
| 75 | + </td> |
| 76 | + </tr> |
| 77 | + <tr> |
| 78 | + <td align="center"> |
| 79 | + <a href="https://kaggle.com/kernels/welcome?src=https://github.com/visual-layer/fastdup/blob/main/examples/analysing-image-classification-dataset.ipynb"> |
| 80 | + <img src="./gallery/kaggle_logo.png" height="25"> |
| 81 | + </a> |
| 82 | + </td> |
| 83 | + </tr> |
| 84 | + <!-- ------------------------------------------------------------------- --> |
| 85 | + <tr> |
| 86 | + <td rowspan="4" width="160"> |
| 87 | + <a href="https://visual-layer.readme.io/docs/objects-and-bounding-boxes"> |
| 88 | + <img src="./gallery/coco_thumbnail.jpg" width="200"> |
| 89 | + </a> |
| 90 | + </td> |
| 91 | + <td rowspan="4"> |
| 92 | + <b>🎁 Analyze Object Detection Dataset:</b> Learn how to load bounding box annotations for object detection and analyze for potential issues. If you have a COCO-style labeled object detection dataset, give this example a try. |
| 93 | + <br> |
| 94 | + <br> |
| 95 | + <b>📌 Dataset:</b> <a href="https://cocodataset.org/#home">COCO</a>. |
| 96 | + </td> |
| 97 | + <td align="center" width="80"> |
| 98 | + <a href="https://nbviewer.org/github/visual-layer/fastdup/blob/main/examples/analyzing-object-detection-dataset.ipynb"> |
| 99 | + <img src="./gallery/nbviewer_logo.png" height="30"> |
| 100 | + </a> |
| 101 | + </td> |
| 102 | + </tr> |
| 103 | + <tr> |
| 104 | + <td align="center"> |
| 105 | + <a href="https://github.com/visual-layer/fastdup/blob/main/examples/analyzing-object-detection-dataset.ipynb"> |
| 106 | + <img src="./gallery/github_logo.png" height="25"> |
| 107 | + </a> |
| 108 | + </td> |
| 109 | + </tr> |
| 110 | + <tr> |
| 111 | + <td align="center"> |
| 112 | + <a href="https://colab.research.google.com/github/visual-layer/fastdup/blob/main/examples/analyzing-object-detection-dataset.ipynb"> |
| 113 | + <img src="./gallery/colab_logo.png" height="20"> |
| 114 | + </a> |
| 115 | + </td> |
| 116 | + </tr> |
| 117 | + <tr> |
| 118 | + <td align="center"> |
| 119 | + <a href="https://kaggle.com/kernels/welcome?src=https://github.com/visual-layer/fastdup/blob/main/examples/analyzing-object-detection-dataset.ipynb"> |
| 120 | + <img src="./gallery/kaggle_logo.png" height="25"> |
| 121 | + </a> |
| 122 | + </td> |
| 123 | + </tr> |
| 124 | + <!-- ------------------------------------------------------------------- --> |
| 125 | +</table> |
| 126 | + |
1 | 127 | ## Load Data From Sources |
2 | 128 | The notebooks in this section show how to load data from various sources and analyze them with fastdup. |
3 | 129 |
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