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license: cc-by-nc-4.0
tags:
- glass-surface-detection
- segmentation
- pytorch
datasets:
- garrying/GSD
---
# GlassNet — pretrained on GSD
Pretrained checkpoint for **GlassNet** 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/
## Files
| File | Description |
|------|-------------|
| `GSD.pth` | GlassNet weights trained on the GSD training split |
## Usage
```python
import torch
from huggingface_hub import hf_hub_download
from model import GlassNet
ckpt = hf_hub_download("garrying/GSD-GlassNet", "GSD.pth")
net = GlassNet()
net.load_state_dict(torch.load(ckpt, map_location="cpu"))
net.eval()
```
For full inference code (data loading, CRF post-processing, saving outputs) see `infer.py` in the original release.
## 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
## Dataset
The GSD dataset is available at [garrying/GSD](https://huggingface.co/datasets/garrying/GSD).
## 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
|