Datasets:
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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
---
# 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
```
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
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