CS604 2026 course project data

.gitattributes

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+*.arrow filter=lfs diff=lfs merge=lfs -text
+*.bin filter=lfs diff=lfs merge=lfs -text
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+*.safetensors filter=lfs diff=lfs merge=lfs -text
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+*tfevents* filter=lfs diff=lfs merge=lfs -text
+# Audio files - uncompressed
+*.pcm filter=lfs diff=lfs merge=lfs -text
+*.sam filter=lfs diff=lfs merge=lfs -text
+*.raw filter=lfs diff=lfs merge=lfs -text
+# Audio files - compressed
+*.aac filter=lfs diff=lfs merge=lfs -text
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+*.mp3 filter=lfs diff=lfs merge=lfs -text
+*.ogg filter=lfs diff=lfs merge=lfs -text
+*.wav filter=lfs diff=lfs merge=lfs -text
+# Image files - uncompressed
+*.bmp filter=lfs diff=lfs merge=lfs -text
+*.gif filter=lfs diff=lfs merge=lfs -text
+*.png filter=lfs diff=lfs merge=lfs -text
+*.tiff filter=lfs diff=lfs merge=lfs -text
+# Image files - compressed
+*.jpg filter=lfs diff=lfs merge=lfs -text
+*.jpeg filter=lfs diff=lfs merge=lfs -text
+*.webp filter=lfs diff=lfs merge=lfs -text
+# Video files - compressed
+*.mp4 filter=lfs diff=lfs merge=lfs -text
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+train.txt filter=lfs diff=lfs merge=lfs -text

README.md

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+# 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.

metadata.json

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+{
+  "dataset_name": "cs604-course-data",
+  "description": "Anonymized image classification dataset with 10,000 classes",
+  "num_classes": 10000,
+  "train": {
+    "num_samples": 450000,
+    "samples_per_class": 45,
+    "label_available": true
+  },
+  "val": {
+    "num_samples": 50000,
+    "samples_per_class": 5,
+    "label_available": false,
+    "note": "Validation labels withheld for benchmark evaluation"
+  },
+  "test": {
+    "num_samples": 100000,
+    "samples_per_class": 10,
+    "label_available": false,
+    "note": "Released when Phase 2 opens; labels withheld for benchmark evaluation"
+  },
+  "image_format": "jpg",
+  "task": "ViT model adaptation"
+}

train.tar.gz

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train.txt

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val.tar.gz

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