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README.md

@@ -1,35 +1,132 @@
 ---
-dataset_info:
-  features:
-  - name: image_id
-    dtype: string
-  - name: image
-    dtype: image
-  - name: mask
-    dtype: image
-  - name: reflections
-    dtype: image
-  - name: edge
-    dtype: image
-  splits:
-  - name: train
-    num_bytes: 371281123
-    num_examples: 2710
-  - name: extra
-    num_bytes: 70697560
-    num_examples: 579
-  - name: test
-    num_bytes: 33643740
-    num_examples: 813
-  download_size: 435116512
-  dataset_size: 475622423
+license: cc-by-nc-4.0
+task_categories:
+  - image-segmentation
+tags:
+  - glass-surface-detection
+  - segmentation
+  - reflection
+  - computer-vision
+pretty_name: Glass Surface Detection (GSD)
+size_categories:
+  - 1K<n<10K
 configs:
-- config_name: default
-  data_files:
-  - split: train
-    path: data/train-*
-  - split: extra
-    path: data/extra-*
-  - split: test
-    path: data/test-*
+  - config_name: default
+    data_files:
+      - split: train
+        path: train/metadata.jsonl
+      - split: extra
+        path: extra/metadata.jsonl
+      - split: test
+        path: test/metadata.jsonl
 ---
+
+# Glass Surface Detection (GSD) Dataset
+
+Dataset from the CVPR 2021 paper:
+
+> **Rich Context Aggregation with Reflection Prior for Glass Surface Detection**  
+> Jiaying Lin, Zebang He, Rynson W.H. Lau  
+> *Proceedings of CVPR 2021*  
+> Project page: https://jiaying.link/cvpr2021-gsd/
+
+## Dataset Summary
+
+GSD is a large-scale benchmark for glass surface detection in the wild. The dataset contains three splits:
+
+- **train** — 2,710 images with full annotations (mask, reflection, edge). This is the split used to train GlassNet.
+- **extra** — 579 additional images with mask and edge annotations but no reflections. Not used to train GlassNet.
+- **test** — 813 images with mask annotations for evaluation.
+
+## Dataset Structure
+
+| Split | Images | Masks | Reflections | Edges |
+|-------|-------:|------:|------------:|------:|
+| train | 2,710  | 2,710 | 2,710       | 2,710 |
+| extra |   579  |   579 | —           |   579 |
+| test  |   813  |   813 | —           | —     |
+
+### Fields
+
+- **image_id** — original filename stem (e.g. `glass_0001`), unique within each split
+- **image** — RGB photograph containing glass surfaces
+- **mask** — binary segmentation mask (white = glass)
+- **reflections** — RGB reflection image paired with the scene (train only; `None` otherwise)
+- **edge** — edge annotation map (train and extra only; `None` for test)
+
+## Usage
+
+```python
+from datasets import load_dataset
+
+ds = load_dataset("garrying/GSD")
+sample = ds["train"][0]
+sample["image_id"]     # original filename stem, e.g. "glass_0001"
+sample["image"]        # PIL Image
+sample["mask"]         # PIL Image (binary mask)
+sample["reflections"]  # PIL Image (None for extra/test)
+sample["edge"]         # PIL Image (None for test)
+```
+
+## Converting Back to Raw Files
+
+A helper script [`parquet_to_raw.py`](parquet_to_raw.py) is included in this repository to convert the dataset back to a folder of raw image files:
+
+```bash
+# download the script
+huggingface-cli download garrying/GSD parquet_to_raw.py --repo-type dataset --local-dir .
+
+# convert all splits to raw PNG files
+python parquet_to_raw.py --repo garrying/GSD --out GSD
+
+# optionally re-upload the raw files to another HF dataset repo
+python parquet_to_raw.py --repo garrying/GSD --out GSD --upload-to your-username/GSD-raw
+```
+
+Output layout:
+```
+GSD/
+  train/  image/  mask/  reflections/  edge/  metadata.jsonl
+  extra/  image/  mask/  edge/          metadata.jsonl
+  test/   image/  mask/  metadata.jsonl
+```
+
+## Pretrained Model
+
+A pretrained **GlassNet** checkpoint (`GSD.pth`) is available in the companion model repository:
+
+👉 [garrying/GSD-GlassNet](https://huggingface.co/garrying/GSD-GlassNet)
+
+### Inference
+
+```bash
+# download the checkpoint
+huggingface-cli download garrying/GSD-GlassNet GSD.pth --local-dir .
+
+# run inference
+python infer.py
+```
+
+## Model Architecture
+
+**GlassNet** uses a ResNeXt-101 backbone with:
+
+- **DenseContrastModule** — multi-scale dilated convolutions (rates 1/2/4/8) with pairwise feature subtraction to capture cross-context contrast
+- **SELayer** — grouped squeeze-and-excitation for context-aware channel reweighting
+- **RefNet** — a lightweight U-Net-style decoder that jointly predicts the binary glass mask and reconstructs the reflection image as auxiliary output
+- **CRF post-processing** — dense CRF refinement of predicted masks at inference time
+
+## Citation
+
+```bibtex
+@inproceedings{GSD:2021,
+    title     = {Rich Context Aggregation with Reflection Prior for Glass Surface Detection},
+    author    = {Lin, Jiaying and He, Zebang and Lau, Rynson W.H.},
+    booktitle = {Proc. CVPR},
+    year      = {2021}
+}
+```
+
+## Contact
+
+jiayinlin5-c@my.cityu.edu.hk