README.md

---
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.