doem1997/cs604-course-data

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

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.