| --- |
| license: mit |
| library_name: segmentation_models_pytorch |
| pipeline_tag: image-segmentation |
| tags: |
| - semantic-segmentation |
| - microstructure |
| - materials-science |
| - pytorch |
| - unet |
| --- |
| |
| # U-Net for Quantitative Microstructure Segmentation |
|
|
| ## Model Description |
|
|
| This repository contains seven dataset-specific U-Net checkpoints used in a |
| benchmark of microstructure segmentation methods. The model is implemented |
| with `segmentation_models_pytorch` and uses 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/Best_Model.pth` | |
| | EMPS | Binary segmentation | 1 | `checkpoints/EMPS/Best_Model.pth` | |
| | Grain | Binary segmentation | 1 | `checkpoints/Grain/Best_Model.pth` | |
| | EBC | 3-class segmentation | 3 | `checkpoints/EBC/Best_Model.pth` | |
| | Super | 3-class segmentation | 3 | `checkpoints/Super/Best_Model.pth` | |
| | MetalDAM | 5-class segmentation | 5 | `checkpoints/MetalDAM/Best_Model.pth` | |
| | UHCS | 7-class segmentation | 7 | `checkpoints/UHCS/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 |
|
|
| Download a checkpoint and run the matching test entry point from the source |
| repository. Example for binary EMPS segmentation: |
|
|
| ```bash |
| hf download NAMESPACE/microstructure-unet checkpoints/EMPS/Best_Model.pth --local-dir weights/unet |
| python CNN/U-net/predict.py \ |
| --dataset-root /path/to/EMPS \ |
| --checkpoint weights/unet/checkpoints/EMPS/Best_Model.pth \ |
| --output-dir results/unet_emps |
| ``` |
|
|
| For EBC, Super, MetalDAM, and UHCS, use `CNN/U-net/test_mutil.py` and pass the |
| matching `--num-classes` value from the table. |
|
|
| ## Evaluation |
|
|
| The public evaluation scripts restore predictions to the original resolution, |
| save raw masks, and calculate mIoU, Dice, precision, recall, accuracy, HD95, |
| Hausdorff distance, NSD, MAE, MBSS, and MBSS_add using `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 comparative |
| evaluation on the named microstructure datasets. They are dataset-specific and |
| should not be treated as general-purpose materials segmentation models. Image |
| acquisition conditions, magnification, annotation conventions, and unseen |
| microstructures may cause substantial performance degradation. 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 also remain subject to their |
| upstream terms. A paper citation will be added after publication. |
| |
