Upload model card and dataset checkpoints

CHECKSUMS.sha256

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+b5b57ff689d343a1e50361e728aa5a6075d5b2c611732ed2638a7220c31dfb84  checkpoints/Aachen-Heerlen/DeepLabV3P_Best_Model.pth
+a2f860d5bbfea1738da8f59f79ec59f7a9b1c77dd5887b7e441c0bd844197a9d  checkpoints/EBC/DeepLabV3P_Best_Model.pth
+8cdffae1cb0a1618608c980531f5a58a4645c33ecc6f5e5e6abd3c0fdcf35352  checkpoints/EMPS/DeepLabV3P_Best_Model.pth
+6d4228297dc63271f256348d5920f8c5b686152ba0e41277ee90dfba6c091757  checkpoints/Grain/DeepLabV3P_Best_Model.pth
+f44c9ec082114012fc287767f8c3b786d99f2deb46c4b62b55a46d01db806608  checkpoints/MetalDAM/DeepLabV3P_Best_Model.pth
+83b1c9404a285e6c657762ee7ad03e1458c5bf14f609cc1c81582825bbff9562  checkpoints/Super/DeepLabV3P_Best_Model.pth
+3a846d500e2c1ed875abd47f3ccc911dd26084bd6de66123bc4da8851b5d6823  checkpoints/UHCS/DeepLabV3P_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
+- deeplabv3plus
+---
+
+# 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/microstructure-segmentation-benchmark
+
+## 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:
+
+```bash
+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.

checkpoint_manifest.json

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+{
+  "model": "DeepLabV3+",
+  "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/DeepLabV3P_Best_Model.pth",
+      "size_bytes": 107075735,
+      "sha256": "b5b57ff689d343a1e50361e728aa5a6075d5b2c611732ed2638a7220c31dfb84",
+      "requires_base_checkpoint": "none"
+    },
+    {
+      "dataset": "EBC",
+      "task": "multiclass_semantic_segmentation",
+      "num_classes": 3,
+      "fine_tuning_route": "full_model",
+      "path": "checkpoints/EBC/DeepLabV3P_Best_Model.pth",
+      "size_bytes": 107077783,
+      "sha256": "a2f860d5bbfea1738da8f59f79ec59f7a9b1c77dd5887b7e441c0bd844197a9d",
+      "requires_base_checkpoint": "none"
+    },
+    {
+      "dataset": "EMPS",
+      "task": "binary_segmentation",
+      "num_classes": 2,
+      "fine_tuning_route": "full_model",
+      "path": "checkpoints/EMPS/DeepLabV3P_Best_Model.pth",
+      "size_bytes": 107075735,
+      "sha256": "8cdffae1cb0a1618608c980531f5a58a4645c33ecc6f5e5e6abd3c0fdcf35352",
+      "requires_base_checkpoint": "none"
+    },
+    {
+      "dataset": "Grain",
+      "task": "binary_segmentation",
+      "num_classes": 2,
+      "fine_tuning_route": "full_model",
+      "path": "checkpoints/Grain/DeepLabV3P_Best_Model.pth",
+      "size_bytes": 107075735,
+      "sha256": "6d4228297dc63271f256348d5920f8c5b686152ba0e41277ee90dfba6c091757",
+      "requires_base_checkpoint": "none"
+    },
+    {
+      "dataset": "MetalDAM",
+      "task": "multiclass_semantic_segmentation",
+      "num_classes": 5,
+      "fine_tuning_route": "full_model",
+      "path": "checkpoints/MetalDAM/DeepLabV3P_Best_Model.pth",
+      "size_bytes": 107079831,
+      "sha256": "f44c9ec082114012fc287767f8c3b786d99f2deb46c4b62b55a46d01db806608",
+      "requires_base_checkpoint": "none"
+    },
+    {
+      "dataset": "Super",
+      "task": "multiclass_semantic_segmentation",
+      "num_classes": 3,
+      "fine_tuning_route": "full_model",
+      "path": "checkpoints/Super/DeepLabV3P_Best_Model.pth",
+      "size_bytes": 107077783,
+      "sha256": "83b1c9404a285e6c657762ee7ad03e1458c5bf14f609cc1c81582825bbff9562",
+      "requires_base_checkpoint": "none"
+    },
+    {
+      "dataset": "UHCS",
+      "task": "multiclass_semantic_segmentation",
+      "num_classes": 7,
+      "fine_tuning_route": "full_model",
+      "path": "checkpoints/UHCS/DeepLabV3P_Best_Model.pth",
+      "size_bytes": 107081879,
+      "sha256": "3a846d500e2c1ed875abd47f3ccc911dd26084bd6de66123bc4da8851b5d6823",
+      "requires_base_checkpoint": "none"
+    }
+  ]
+}

checkpoints/Aachen-Heerlen/DeepLabV3P_Best_Model.pth

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