Upload model card and dataset checkpoints

CHECKSUMS.sha256

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+e9b58b033d2387cc2c2b2371044c9256dc60e676e68ece8660cbec8620d6b796  checkpoints/Aachen-Heerlen/best_mIoU_epoch_76.pth
+8e409a056d869907021f9bf208e16fe4a7a7b7c469c83ff79455a05552cb6392  checkpoints/EBC/best_mIoU_epoch_128.pth
+d66d7ccf1e05ba21447a3a584fdcd1d8b8e0b87d7582d423e036077dd8f09d2d  checkpoints/EMPS/best_mIoU_epoch_153.pth
+73b823393881630fc95b1a42cbfb25a8708da027bd694ddceda6d7fbba635b1c  checkpoints/Grain/best_mIoU_epoch_63.pth
+619b95528d6510a50b8c67735900df371c532e4ff9871aeb25e85ccb1da32c45  checkpoints/MetalDAM/best_mIoU_epoch_96.pth
+d7b101742362ad602998186ab6e28f7158b822862e85d9dcd5b6084774228167  checkpoints/Super/best_mIoU_epoch_170.pth
+59f2fcdbe48c0dedf4bacd80026a2e499f56a690a07e561d2acae320fb899736  checkpoints/UHCS/best_mIoU_epoch_137.pth

LICENSE

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+MIT License
+
+Copyright (c) 2026 The Material Image Segmentation Benchmark Authors
+
+Permission is hereby granted, free of charge, to any person obtaining a copy
+of this software and associated documentation files (the "Software"), to deal
+in the Software without restriction, including without limitation the rights
+to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
+copies of the Software, and to permit persons to whom the Software is
+furnished to do so, subject to the following conditions:
+
+The above copyright notice and this permission notice shall be included in all
+copies or substantial portions of the Software.
+
+THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
+IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
+FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
+AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
+LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
+OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
+SOFTWARE.

README.md

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+---
+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/microstructure-segmentation-benchmark
+
+## 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.

checkpoint_manifest.json

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+{
+  "model": "SegFormer-B0",
+  "source_code": "https://github.com/WUT-AI-AI4Mat/microstructure-segmentation-benchmark",
+  "checkpoint_count": 7,
+  "checkpoints": [
+    {
+      "dataset": "Aachen-Heerlen",
+      "task": "binary_segmentation",
+      "num_classes": 2,
+      "fine_tuning_route": "full_model",
+      "path": "checkpoints/Aachen-Heerlen/best_mIoU_epoch_76.pth",
+      "size_bytes": 21396082,
+      "sha256": "e9b58b033d2387cc2c2b2371044c9256dc60e676e68ece8660cbec8620d6b796",
+      "requires_base_checkpoint": "none"
+    },
+    {
+      "dataset": "EBC",
+      "task": "multiclass_semantic_segmentation",
+      "num_classes": 3,
+      "fine_tuning_route": "full_model",
+      "path": "checkpoints/EBC/best_mIoU_epoch_128.pth",
+      "size_bytes": 15111922,
+      "sha256": "8e409a056d869907021f9bf208e16fe4a7a7b7c469c83ff79455a05552cb6392",
+      "requires_base_checkpoint": "none"
+    },
+    {
+      "dataset": "EMPS",
+      "task": "binary_segmentation",
+      "num_classes": 2,
+      "fine_tuning_route": "full_model",
+      "path": "checkpoints/EMPS/best_mIoU_epoch_153.pth",
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+      "requires_base_checkpoint": "none"
+    },
+    {
+      "dataset": "Grain",
+      "task": "binary_segmentation",
+      "num_classes": 2,
+      "fine_tuning_route": "full_model",
+      "path": "checkpoints/Grain/best_mIoU_epoch_63.pth",
+      "size_bytes": 17518130,
+      "sha256": "73b823393881630fc95b1a42cbfb25a8708da027bd694ddceda6d7fbba635b1c",
+      "requires_base_checkpoint": "none"
+    },
+    {
+      "dataset": "MetalDAM",
+      "task": "multiclass_semantic_segmentation",
+      "num_classes": 5,
+      "fine_tuning_route": "full_model",
+      "path": "checkpoints/MetalDAM/best_mIoU_epoch_96.pth",
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+    },
+    {
+      "dataset": "Super",
+      "task": "multiclass_semantic_segmentation",
+      "num_classes": 3,
+      "fine_tuning_route": "full_model",
+      "path": "checkpoints/Super/best_mIoU_epoch_170.pth",
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+      "sha256": "d7b101742362ad602998186ab6e28f7158b822862e85d9dcd5b6084774228167",
+      "requires_base_checkpoint": "none"
+    },
+    {
+      "dataset": "UHCS",
+      "task": "multiclass_semantic_segmentation",
+      "num_classes": 7,
+      "fine_tuning_route": "full_model",
+      "path": "checkpoints/UHCS/best_mIoU_epoch_137.pth",
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+      "sha256": "59f2fcdbe48c0dedf4bacd80026a2e499f56a690a07e561d2acae320fb899736",
+      "requires_base_checkpoint": "none"
+    }
+  ]
+}

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