| # CS604 Course Project Dataset |
|
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| For the task "ViT Model Adaptation". |
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|
| ## Overview |
| An anonymized fine-grained image classification dataset with 10,000 classes. |
|
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| ## Dataset Statistics |
| - **Number of classes**: 10,000 |
| - **Training samples**: 450,000 (45 per class, with labels) |
| - **Validation samples**: 50,000 (5 per class, **without labels**) — Phase 1 |
| - **Test samples**: 100,000 (10 per class, **without labels**) — released when Phase 2 opens |
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|
| ## Files |
| - `train.tar.gz` + `train.txt`: training images and labels (format: `train/class_XXXX/filename.jpg class_id`) |
| - `val.tar.gz` + `val.txt`: validation images and image paths only (format: `val/image_XXXXXX.jpg`) |
| - `test.tar.gz` + `test.txt`: added when Phase 2 opens |
| - `metadata.json`: dataset metadata |
|
|
| ``` |
| train/ |
| class_0000/ # 45 images |
| ... |
| class_9999/ |
| val/ |
| image_000000.jpg |
| ... |
| image_049999.jpg |
| ``` |
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| File names in `val/` and `test/` are random and carry no information about the class. |
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|
| ## Download |
| ```bash |
| pip install -U huggingface_hub |
| hf download doem1997/cs604-course-data --repo-type dataset --local-dir cs604-course-data |
| cd cs604-course-data && tar -xzf train.tar.gz && tar -xzf val.tar.gz |
| ``` |
|
|
| ## Submission |
| Predict a class for every image of the split and submit to the CodaBench competition (link in the course slides). |
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| One line per image, `<image_path> <predicted_class_id>`, with `class_id` in [0, 9999]: |
| ``` |
| val/image_000000.jpg 1234 |
| val/image_000001.jpg 5678 |
| ... |
| ``` |
| Name the file `val_team_XX.txt` (Phase 1) or `test_team_XX.txt` (Phase 2), zip it, and upload the zip. |
| Every image of the split must appear exactly once. |
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| Validation and test labels are withheld. Do not label validation/test images by hand or train on them. |
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