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README.md

@@ -1,28 +1,108 @@
 ---
-dataset_info:
-  features:
-  - name: image_id
-    dtype: string
-  - name: image
-    dtype: image
-  - name: mask
-    dtype: image
-  - name: depth
-    dtype: image
-  splits:
-  - name: train
-    num_bytes: 671199548
-    num_examples: 2400
-  - name: test
-    num_bytes: 367134292
-    num_examples: 609
-  download_size: 1942993580
-  dataset_size: 1038333840
-configs:
-- config_name: default
-  data_files:
-  - split: train
-    path: data/train-*
-  - split: test
-    path: data/test-*
+license: cc-by-nc-4.0
+task_categories:
+  - image-segmentation
+tags:
+  - glass-surface-detection
+  - rgb-d
+  - depth-estimation
+  - scene-understanding
+pretty_name: RGBD-GSD (RGB-D Glass Surface Detection Dataset)
+size_categories:
+  - 1K<n<10K
 ---
+
+# RGBD-GSD — RGB-D Glass Surface Detection Dataset
+
+RGBD-GSD is the first large-scale **RGB-D glass surface detection** dataset, introduced in:
+
+> **Leveraging RGB-D Data with Cross-Modal Context Mining for Glass Surface Detection**  
+> Jiaying Lin\*, Yuen-Hei Yeung\*, Shuquan Ye, Rynson W. H. Lau  
+> AAAI 2025  
+> [arXiv](https://arxiv.org/abs/2206.11250) · [Project Page](https://jiaying.link/aaai2025-rgbdglass/)
+
+## Dataset Summary
+
+RGBD-GSD contains **3,009 RGB-D images** across a wide range of real-world glass surface categories, each paired with a precise binary segmentation mask and a depth map. Depth maps are captured with 3D sensors; blank (missing) regions in depth correspond to glass surfaces, providing a complementary detection cue to the RGB image.
+
+| Split | Samples |
+|-------|--------:|
+| train | 2,400   |
+| test  |   609   |
+| **total** | **3,009** |
+
+## Dataset Structure
+
+Each sample has four columns:
+
+| Column     | Type   | Description |
+|------------|--------|-------------|
+| `image_id` | string | Original filename stem, e.g. `00000001`. Enables round-trip fidelity. |
+| `image`    | Image  | JPEG RGB image |
+| `mask`     | Image  | PNG binary segmentation mask (glass = white, background = black) |
+| `depth`    | Image  | PNG depth map (blank/missing regions often correspond to glass surfaces) |
+
+The original on-disk layout is:
+```
+RGBD-GSD/
+  train/
+    images/   # {id}.jpg
+    masks/    # {id}.png
+    depths/   # {id}.png
+  test/
+    …
+```
+
+## Loading the Dataset
+
+```python
+from datasets import load_dataset
+
+ds = load_dataset("garrying/RGBD-GSD")
+# or load a single split:
+train_ds = load_dataset("garrying/RGBD-GSD", split="train")
+test_ds  = load_dataset("garrying/RGBD-GSD", split="test")
+
+sample = train_ds[0]
+print(sample["image_id"])   # e.g. "00000001"
+sample["image"].show()
+sample["mask"].show()
+sample["depth"].show()
+```
+
+## Converting Back to Raw Files
+
+A helper script `parquet_to_raw.py` is included in this repo to restore the original directory structure:
+
+```bash
+# Download the helper
+huggingface-cli download garrying/RGBD-GSD parquet_to_raw.py --repo-type dataset
+
+# Restore all splits from HuggingFace
+python parquet_to_raw.py --repo garrying/RGBD-GSD
+
+# Restore only the test split to a custom directory
+python parquet_to_raw.py --repo garrying/RGBD-GSD --splits test --out RGBD-GSD_test
+```
+
+Output structure matches the original:
+```
+RGBD-GSD/
+  train/images/{id}.jpg  train/masks/{id}.png  train/depths/{id}.png
+  test/…
+```
+
+## Citation
+
+```bibtex
+@article{aaai2025_rgbdglass,
+  author    = {Lin, Jiaying and Yeung, Yuen-Hei and Ye, Shuquan and Lau, Rynson W.H.},
+  title     = {Leveraging RGB-D Data with Cross-Modal Context Mining for Glass Surface Detection},
+  journal   = {AAAI},
+  year      = {2025},
+}
+```
+
+## License
+
+This dataset is released under [CC BY-NC 4.0](https://creativecommons.org/licenses/by-nc/4.0/). Non-commercial use only.