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garrying/
GlassSemNet

glass-surface-detection

semantic-segmentation

scene-understanding

pytorch

cc-by-nc-4.0

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GlassSemNet — Glass Semantic Network

Pre-trained weights for GlassSemNet, introduced in:

Exploiting Semantic Relations for Glass Surface Detection
Jiaying Lin, Yuen-Hei Yeung, Rynson W. H. Lau
NeurIPS 2022
Paper · Project Page · Dataset (GSD-S)

Model Summary

GlassSemNet detects glass surfaces by exploiting semantic relations between the glass region and its surrounding scene context. It uses a dual-backbone design:

Spatial backbone (SegFormer): extracts multi-scale spatial features.
Semantic backbone (ResNet-50 + DeepLabV3+): encodes 43-class semantic scene features into compact per-class encodings.
Semantic-Aware Attention (SAA): fuses spatial and semantic features at three scales using the semantic encodings as guidance.
Cross-modal Context Aggregation (CCA): aggregates cross-scale context at the deepest level.
UPerNet decoder: combines the fused multi-scale features into the final glass surface prediction.

File	Description
`GlassSemNet.pth`	Best checkpoint (917 MB), saved as a raw `state_dict`

Loading the Weights

python

import torch
from model.GlassSemNet import GlassSemNet   # from the code release

model = GlassSemNet()
state_dict = torch.load("GlassSemNet.pth", map_location="cpu")
model.load_state_dict(state_dict)
model.eval()

Download the checkpoint:

huggingface-cli download garrying/GlassSemNet GlassSemNet.pth --local-dir ./weights

Inference

python predict.py -c GlassSemNet.pth -i /path/to/images/ -o /path/to/output/

Images are resized to 384 × 384 internally. Predictions are post-processed with CRF refinement and thresholded to produce binary glass surface masks.

Training Dataset

This model was trained and evaluated on GSD-S, the first glass surface detection dataset with semantic annotations:

4,519 images (3,511 train / 1,008 test) with binary glass masks, instance segmentation maps, and 43-class semantic labels
Available at garrying/GSD-S

Citation

bibtex

@article{neurips2022:gsds2022,
  author    = {Lin, Jiaying and Yeung, Yuen-Hei and Lau, Rynson W.H.},
  title     = {Exploiting Semantic Relations for Glass Surface Detection},
  journal   = {NeurIPS},
  year      = {2022},
}

License

Non-commercial use only — CC BY-NC 4.0.