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README.md

@@ -1,30 +1,140 @@
 ---
-dataset_info:
-  features:
-  - name: image_id
-    dtype: string
-  - name: image
-    dtype: image
-  - name: mask
-    dtype: image
-  - name: seg
-    dtype: image
-  - name: seg_colored
-    dtype: image
-  splits:
-  - name: train
-    num_bytes: 683594277
-    num_examples: 3911
-  - name: test
-    num_bytes: 63527420
-    num_examples: 608
-  download_size: 597110534
-  dataset_size: 747121697
-configs:
-- config_name: default
-  data_files:
-  - split: train
-    path: data/train-*
-  - split: test
-    path: data/test-*
+license: other
+license_name: bsd-3-clause-non-commercial
+license_link: https://github.com/Mhaiyang/NeurIPS2022_GlassSemNet/blob/main/LICENSE
+task_categories:
+  - image-segmentation
+tags:
+  - glass-surface-detection
+  - semantic-segmentation
+  - scene-understanding
+pretty_name: GSD-S (Glass Surface Detection – Semantics)
+size_categories:
+  - 1K<n<10K
 ---
+
+# GSD-S: Glass Surface Detection – Semantics
+
+GSD-S is a glass surface detection dataset augmented with per-pixel semantic labels, introduced in the NeurIPS 2022 paper **"Exploiting Semantic Relations for Glass Surface Detection"**.
+Each sample pairs an RGB photograph with a binary glass mask and a 43-class semantic segmentation map, enabling joint glass detection and scene-semantic reasoning.
+
+- **Paper:** [Exploiting Semantic Relations for Glass Surface Detection](https://openreview.net/forum?id=WrIrYMCZgbb) — NeurIPS 2022
+- **Project page:** https://jiaying.link/neurips2022-gsds/
+- **Authors:** Jiaying Lin, Yuen-Hei Yeung, Rynson W.H. Lau (City University of Hong Kong)
+
+---
+
+## Dataset Summary
+
+| Split | Samples |
+|-------|---------|
+| train | 3,911   |
+| test  |   608   |
+| **total** | **4,519** |
+
+Images are 640 × 480 pixels (JPEG). All annotation maps are PNG.
+
+---
+
+## Columns
+
+| Column | Type | Description |
+|--------|------|-------------|
+| `image_id` | `string` | Original filename stem (e.g. `000000000711`); use for round-trip fidelity |
+| `image` | `Image` | RGB photograph (.jpg) |
+| `mask` | `Image` | Binary glass mask — pixel values 0 (non-glass) or 255 (glass) |
+| `seg` | `Image` | Semantic segmentation map — pixel values 0–42 (class index) |
+| `seg_colored` | `Image` | False-color rendering of `seg` using the GSD-S palette (for visualization) |
+
+### Semantic classes (43 total)
+
+`unknown`, `wall`, `glass`, `floor`, `ceiling`, `door`, `chair`, `table`, `sofa`,
+`cabinet`, `curtain`, `blinds`, `bedding`, `picture`, `light`, `clothes`, `counter`,
+`sink`, `toilet`, `towel`, `mirror`, `tv`, `building_structure`, `stationery`, `plant`,
+`person`, `fridge`, `bath_shower`, `seat`, `floor_mat`, `fence`, `ground`, `bottle`,
+`kitchenware`, `road`, `transport`, `electronics`, `food`, `bag`, `nature`, `animal`,
+`road_infrastructure`, `clock`
+
+The class-to-color mapping is available in the official repository at
+`utils/GSD-S_color_map.csv`.
+
+---
+
+## Loading the Dataset
+
+```python
+from datasets import load_dataset
+
+ds = load_dataset("garrying/GSD-S")
+sample = ds["train"][0]
+
+print(sample["image_id"])   # e.g. "000000000711"
+sample["image"].show()      # RGB photo
+sample["mask"].show()       # binary glass mask
+sample["seg"].show()        # semantic class indices
+sample["seg_colored"].show() # false-color visualization
+```
+
+---
+
+## Converting Back to Raw Files
+
+A conversion helper is bundled in this repository. Download and run it:
+
+```bash
+# Download the script
+huggingface-cli download garrying/GSD-S parquet_to_raw.py --repo-type dataset --local-dir .
+
+# Restore all splits to ./GSD-S/
+python parquet_to_raw.py --repo garrying/GSD-S
+
+# Or restore from a locally cached copy
+python parquet_to_raw.py --local /path/to/local/cache
+```
+
+Output layout:
+
+```
+GSD-S/
+  train/
+    images/         # .jpg
+    masks/          # .png
+    segs/           # .png  (class-index maps)
+    segs_colored/   # .png  (false-color maps)
+  test/
+    ...
+```
+
+---
+
+## Evaluation Metrics
+
+The official evaluation protocol reports:
+
+- **IoU** — Intersection over Union
+- **F-measure** (Fβ, β² = 0.3) — weighted precision-recall
+- **MAE** — Mean Absolute Error
+- **BER** — Balanced Error Rate
+
+Predictions and ground-truth masks are binarized at threshold 0.5 before computing all metrics.
+
+---
+
+## Citation
+
+```bibtex
+@inproceedings{neurips2022:gsds2022,
+  title     = {Exploiting Semantic Relations for Glass Surface Detection},
+  author    = {Lin, Jiaying and Yeung, Yuen Hei and Lau, Rynson W.H.},
+  booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
+  year      = {2022}
+}
+```
+
+---
+
+## License
+
+BSD 3-Clause License — **non-commercial use only**.
+See [LICENSE](https://github.com/Mhaiyang/NeurIPS2022_GlassSemNet/blob/main/LICENSE) for the full text.
+Please cite the paper if you use this dataset.