garrying/GSD-GlassNet

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

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.

Citation

@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