Upload model card and dataset checkpoints

CHECKSUMS.sha256

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+70f3d25e937de33b3aa7480df248f148f515179738051d623d9918ccf338c985  checkpoints/Aachen-Heerlen/Best_Model.pth
+08fdd67970562adf9184486d56f7c1ed1abd6a17ac964b5297e29ee7dcdc3c3c  checkpoints/EBC/Best_Model.pth
+f53ce3d927ba10d5d898efd6aa9f78d7a85351057c824e3b4471addaa9e08008  checkpoints/EMPS/Best_Model.pth
+1e8848fc872b36ef9d7c093477a5a92ada47dfb0e468da85e090ce1b36063fd1  checkpoints/Grain/Best_Model.pth
+f957f80232f02aa0a0b615db37412aadf0909ef959fb1defc8a0a351e33c3778  checkpoints/MetalDAM/Best_Model.pth
+2e2800754c8045b361bf9b7c7c690aa83b5817315a225eaafe5f63e30a36490e  checkpoints/Super/Best_Model.pth
+ce4392bba3bd6653311b2b925684bbb0758f7aa73336ebd78aa13b30370aa18d  checkpoints/UHCS/Best_Model.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: 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/microstructure-segmentation-benchmark
+
+## 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.

checkpoint_manifest.json

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+{
+  "model": "U-Net",
+  "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_Model.pth",
+      "size_bytes": 130438093,
+      "sha256": "70f3d25e937de33b3aa7480df248f148f515179738051d623d9918ccf338c985",
+      "requires_base_checkpoint": "none"
+    },
+    {
+      "dataset": "EBC",
+      "task": "multiclass_semantic_segmentation",
+      "num_classes": 3,
+      "fine_tuning_route": "full_model",
+      "path": "checkpoints/EBC/Best_Model.pth",
+      "size_bytes": 130439245,
+      "sha256": "08fdd67970562adf9184486d56f7c1ed1abd6a17ac964b5297e29ee7dcdc3c3c",
+      "requires_base_checkpoint": "none"
+    },
+    {
+      "dataset": "EMPS",
+      "task": "binary_segmentation",
+      "num_classes": 2,
+      "fine_tuning_route": "full_model",
+      "path": "checkpoints/EMPS/Best_Model.pth",
+      "size_bytes": 130438093,
+      "sha256": "f53ce3d927ba10d5d898efd6aa9f78d7a85351057c824e3b4471addaa9e08008",
+      "requires_base_checkpoint": "none"
+    },
+    {
+      "dataset": "Grain",
+      "task": "binary_segmentation",
+      "num_classes": 2,
+      "fine_tuning_route": "full_model",
+      "path": "checkpoints/Grain/Best_Model.pth",
+      "size_bytes": 130438093,
+      "sha256": "1e8848fc872b36ef9d7c093477a5a92ada47dfb0e468da85e090ce1b36063fd1",
+      "requires_base_checkpoint": "none"
+    },
+    {
+      "dataset": "MetalDAM",
+      "task": "multiclass_semantic_segmentation",
+      "num_classes": 5,
+      "fine_tuning_route": "full_model",
+      "path": "checkpoints/MetalDAM/Best_Model.pth",
+      "size_bytes": 130440397,
+      "sha256": "f957f80232f02aa0a0b615db37412aadf0909ef959fb1defc8a0a351e33c3778",
+      "requires_base_checkpoint": "none"
+    },
+    {
+      "dataset": "Super",
+      "task": "multiclass_semantic_segmentation",
+      "num_classes": 3,
+      "fine_tuning_route": "full_model",
+      "path": "checkpoints/Super/Best_Model.pth",
+      "size_bytes": 130439245,
+      "sha256": "2e2800754c8045b361bf9b7c7c690aa83b5817315a225eaafe5f63e30a36490e",
+      "requires_base_checkpoint": "none"
+    },
+    {
+      "dataset": "UHCS",
+      "task": "multiclass_semantic_segmentation",
+      "num_classes": 7,
+      "fine_tuning_route": "full_model",
+      "path": "checkpoints/UHCS/Best_Model.pth",
+      "size_bytes": 130441549,
+      "sha256": "ce4392bba3bd6653311b2b925684bbb0758f7aa73336ebd78aa13b30370aa18d",
+      "requires_base_checkpoint": "none"
+    }
+  ]
+}

checkpoints/Aachen-Heerlen/Best_Model.pth

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checkpoints/EBC/Best_Model.pth

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