Segmentation Methods for Quantitative Microstructure Analysi
Collection
Fine-tuned checkpoints for eight microstructure segmentation models evaluated on seven datasets. • 8 items • Updated
DeepLabV3+ for Quantitative Microstructure Segmentation
Model Description
This repository contains seven dataset-specific DeepLabV3+ checkpoints used
in a benchmark of microstructure segmentation methods. The implementation is
from segmentation_models_pytorch with a ResNet-50 encoder initialized from
ImageNet weights. Inputs are resized to 512 x 512 pixels and predictions are
restored to the original image size with nearest-neighbor interpolation.
Source code: https://github.com/WUT-AI-AI4Mat/Segmentation-methods-evaluation-for-quantitative-microstructure-analysis
Checkpoints
| Dataset | Task | Output channels | File |
|---|---|---|---|
| Aachen-Heerlen | Binary segmentation | 1 | checkpoints/Aachen-Heerlen/DeepLabV3P_Best_Model.pth |
| EMPS | Binary segmentation | 1 | checkpoints/EMPS/DeepLabV3P_Best_Model.pth |
| Grain | Binary segmentation | 1 | checkpoints/Grain/DeepLabV3P_Best_Model.pth |
| EBC | 3-class segmentation | 3 | checkpoints/EBC/DeepLabV3P_Best_Model.pth |
| Super | 3-class segmentation | 3 | checkpoints/Super/DeepLabV3P_Best_Model.pth |
| MetalDAM | 5-class segmentation | 5 | checkpoints/MetalDAM/DeepLabV3P_Best_Model.pth |
| UHCS | 7-class segmentation | 7 | checkpoints/UHCS/DeepLabV3P_Best_Model.pth |
Binary checkpoints produce one foreground logit; background is represented by the complementary binary label. Multiclass counts include background.
Training Details
- Input size: 512 x 512
- Batch size: 32
- Epochs: 500
- Optimizer: AdamW
- Learning rate: 0.0003
- Weight decay: 0.001
- Scheduler: CosineAnnealingLR with minimum learning rate 0.000001
- Early-stopping patience: 50
- Binary objective: Dice and BCEWithLogits
- Multiclass objective: Dice and cross-entropy
Usage
Example for binary EMPS segmentation:
hf download NAMESPACE/microstructure-deeplabv3plus checkpoints/EMPS/DeepLabV3P_Best_Model.pth --local-dir weights/deeplabv3plus
python "CNN/DeepLabV3+/test.py" \
--dataset-root /path/to/EMPS \
--checkpoint weights/deeplabv3plus/checkpoints/EMPS/DeepLabV3P_Best_Model.pth \
--output-dir results/deeplabv3plus_emps
For EBC, Super, MetalDAM, and UHCS, use
CNN/DeepLabV3+/test_multi.py and pass the matching --num-classes value.
Evaluation
The evaluation scripts save original-resolution masks and report mIoU, Dice,
precision, recall, accuracy, HD95, Hausdorff distance, NSD, MAE, MBSS, and
MBSS_add through Myutils/metrics.py. Numerical benchmark results will be
linked after the associated paper becomes publicly available.
Intended Use and Limitations
These checkpoints are intended for research reproduction and comparison on the named microstructure datasets. They are not general-purpose segmentation models. Domain shift in imaging, magnification, materials, or annotation policy may reduce accuracy. They are not validated for safety-critical or industrial quality-control decisions.
License and Citation
The released experiment files are provided under the MIT license. The model implementation and ImageNet initialization remain subject to upstream terms. A paper citation will be added after publication.