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license: apache-2.0
language:
- en
pipeline_tag: image-segmentation
tags:
- pytorch
- rgb-d
- rail-surface-defect-detection
- sam2
- glide-net
---
# GLIDE-Net
Full PyTorch checkpoint for **Global-Local Interaction and Detail Enhancement
Network for RGB-D Rail Surface Defect Detection**.
Code and installation instructions:
[Jerry050512/GLIDE-Net](https://github.com/Jerry050512/GLIDE-Net).
## Model
GLIDE-Net uses a Transformer–CNN hybrid encoder with a frozen SAM2 Hiera-L
backbone and trainable adapters, Cross-Modal Fusion (CMF), and a Multi-Scale
Detail Enhancement and Fusion (MSDEF) decoder.
`glide_net_6440647.pth` contains the complete model state, including the frozen
backbone and batch normalization buffers. It requires no separate SAM2
checkpoint for inference. File size: 968,608,466 bytes.
This is a custom PyTorch model; use the implementation in the linked GitHub
repository to load it.
## Download and inference
```bash
pip install -U huggingface_hub
hf download jerry050512/GLIDE-Net glide_net_6440647.pth --local-dir checkpoints
git clone https://github.com/Jerry050512/GLIDE-Net.git
cd GLIDE-Net
# Install dependencies as described in the repository README.
python test.py \
--data_path /path/to/NEU-RSDDS-AUG \
--checkpoint /path/to/checkpoints/glide_net_6440647.pth \
--device cpu --cpu_threads 4 --num_workers 0 \
--save_path ./results/pred
python eval.py \
--pred_path ./results/pred \
--gt_path /path/to/NEU-RSDDS-AUG/GT_test
```
The dataset must contain aligned RGB BMP, depth TIFF and GT PNG files in
`Image_test/`, `Depth_test/` and `GT_test/`. Use the original dataset inputs,
rather than JPEG/PNG visualization copies. Input resolution is 352×352.
CPU inference is supported; GPU inference is faster.
## Results
The original experiment's saved predictions on the 362-image NEU RSDDS-AUG
test set yield:
| S-measure | MAE | max-E | max-F |
| --- | --- | --- | --- |
| 0.8718 | 0.0501 | 0.9475 | 0.9129 |
max-F uses the maximum of the averaged F-measure curve with beta=0.3;
max-E uses the maximum of the averaged E-measure curve.
The original experiment used the test split for checkpoint selection.
The checkpoint was selected at epoch 27 and migrated from experiment commit
`64406472f9c90dbf30f4ba44115c1e3a62167699` by renaming all 991 state tensors
without changing their values. Three sampled old/release CPU model outputs
matched exactly at all decoder scales. An 85-image CPU check matched historical
prediction metrics at four decimal places; some pixels differed by one uint8
gray level. This is a subset verification, not a full CPU rerun of 362 images.
## Intended use and limitations
The model is intended for research on RGB-D rail surface defect segmentation.
Performance on other cameras, depth sensors or datasets has not been established.
It has not been validated for autonomous safety-critical rail inspection.
## License and attribution
Released under Apache-2.0; see [LICENSE](LICENSE).
The included frozen backbone derives from
[Meta's SAM2](https://github.com/facebookresearch/sam2), also Apache-2.0.
Copyright (c) Meta Platforms, Inc. and affiliates. All rights reserved.
Dataset rights remain with the original dataset authors.
The checksum of the uploaded checkpoint is provided in `SHA256SUMS.txt`.
Formal citation details will be added after publication.
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