File size: 1,711 Bytes
8e20b7f | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 | # CS604 Course Project Dataset
For the task "ViT Model Adaptation".
## Overview
An anonymized fine-grained image classification dataset with 10,000 classes.
## 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
## 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
```
File names in `val/` and `test/` are random and carry no information about the class.
## 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).
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.
Validation and test labels are withheld. Do not label validation/test images by hand or train on them.
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