| --- |
| license: mit |
| library_name: mmsegmentation |
| pipeline_tag: image-segmentation |
| tags: |
| - semantic-segmentation |
| - microstructure |
| - materials-science |
| - pytorch |
| - segformer |
| - mmsegmentation |
| --- |
| |
| # SegFormer-B0 for Quantitative Microstructure Segmentation |
|
|
| ## Model Description |
|
|
| This repository contains seven dataset-specific SegFormer checkpoints trained |
| with MMSegmentation. The architecture uses a MiT-B0 backbone and SegFormer |
| decode head with 512 x 512 inputs. |
|
|
| Source code and dataset-specific configs: |
| https://github.com/WUT-AI-AI4Mat/Segmentation-methods-evaluation-for-quantitative-microstructure-analysis |
|
|
| ## Checkpoints |
|
|
| | Dataset | Classes | File | |
| | --- | ---: | --- | |
| | Aachen-Heerlen | 2 | `checkpoints/Aachen-Heerlen/best_mIoU_epoch_76.pth` | |
| | EMPS | 2 | `checkpoints/EMPS/best_mIoU_epoch_153.pth` | |
| | Grain | 2 | `checkpoints/Grain/best_mIoU_epoch_63.pth` | |
| | EBC | 3 | `checkpoints/EBC/best_mIoU_epoch_128.pth` | |
| | Super | 3 | `checkpoints/Super/best_mIoU_epoch_170.pth` | |
| | MetalDAM | 5 | `checkpoints/MetalDAM/best_mIoU_epoch_96.pth` | |
| | UHCS | 7 | `checkpoints/UHCS/best_mIoU_epoch_137.pth` | |
|
|
| Class counts include background. |
|
|
| ## Training Details |
|
|
| - Backbone: MiT-B0 |
| - Input size: 512 x 512 |
| - Training batch size: 32 |
| - Validation and test batch size: 16 |
| - Epochs: 200 |
| - Optimizer: AdamW |
| - Learning rate: 0.00006 |
| - Adam betas: 0.9 and 0.999 |
| - Weight decay: 0.01 |
| - Scheduler: five-epoch linear warm-up followed by polynomial decay |
| - Initialization: `mit_b0_20220624-7e0fe6dd.pth` |
|
|
| ## Usage |
|
|
| Install the MMSegmentation environment described in the source repository. |
| Example for EBC: |
|
|
| ```bash |
| hf download NAMESPACE/microstructure-segformer-b0 checkpoints/EBC/best_mIoU_epoch_128.pth --local-dir weights/segformer-b0 |
| cd mmsegmentation |
| export DATASET_ROOT=/path/to/EBC |
| python tools/test_ebc_segformer.py \ |
| --dataset-root /path/to/EBC \ |
| --checkpoint ../weights/segformer-b0/checkpoints/EBC/best_mIoU_epoch_128.pth \ |
| --result-root ../results/segformer_ebc |
| ``` |
|
|
| Use the corresponding dataset config and `tools/test_*_segformer.py` wrapper |
| for the other checkpoints. |
|
|
| ## Evaluation |
|
|
| The shared testing code calls `Myutils.metrics.Metric.compute_all`, saves raw |
| prediction masks, and exports an Excel report. Reported metrics include mIoU, |
| Dice, precision, recall, accuracy, HD95, Hausdorff distance, NSD, MAE, MBSS, |
| and MBSS_add. Numerical benchmark results will be linked after the associated |
| paper becomes publicly available. |
| |
| ## Intended Use and Limitations |
| |
| The checkpoints are intended for reproducing segmentation experiments on the |
| named datasets. Performance may not transfer to unseen materials, imaging |
| modalities, magnifications, or annotation conventions. The models are not |
| validated for safety-critical or industrial quality-control decisions. |
| |
| ## License and Citation |
| |
| The released experiment files are provided under the MIT license. |
| MMSegmentation and the SegFormer initialization remain subject to their |
| upstream licenses. A paper citation will be added after publication. |
| |
