Upload model card and dataset checkpoints

CHECKSUMS.sha256

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+cdb97adbc50608525962d6bf39803c9e13e6d87f5589296db53a86b3765219a2  checkpoints/Aachen-Heerlen/microsam_lora_decoder_best.pth
+a9901554236cbde55fb7bba896e99347a4dd6a15e26e19df0f64fc1fd198f37d  checkpoints/EBC/semantic_microsam_best.pth
+acd96d5a026a3fd3288b191ee92692a22860a0d05b50458457ddf162239daa87  checkpoints/EMPS/microsam_lora_decoder_best.pth
+69f7e3ce2aa76e79bb87818483c33a18bd5a353990f86bcfe77c8e5b94ab3c23  checkpoints/Grain/microsam_lora_decoder_best.pth
+965f1d87cbbb644301595135e4d83726b2242277a68fcea80f433f0888c95193  checkpoints/MetalDAM/semantic_microsam_best.pth
+c55d2f3df5534b56b49584f6de879c782a81a4096273ccca4409e16c6136c258  checkpoints/Super/semantic_microsam_best.pth
+dc541c823ea9f55bc932c6c1beb5b58d7341f545dffb78ff0e2dc6d388fae4eb  checkpoints/UHCS/semantic_microsam_best.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: pytorch
+pipeline_tag: image-segmentation
+tags:
+- semantic-segmentation
+- microstructure
+- microscopy
+- materials-science
+- segment-anything
+- lora
+- micro-sam
+---
+
+# MicroSAM Fine-Tuned Checkpoints for Microstructure Segmentation
+
+## Model Description
+
+This repository contains dataset-specific fine-tuned parameter checkpoints for
+the MicroSAM `vit_b_lm` model. The files contain fine-tuned parameters rather
+than a complete base model. The matching scripts obtain the `vit_b_lm` base
+model through `micro_sam.util.get_sam_model` and then load these parameters.
+
+Source code:
+https://github.com/WUT-AI-AI4Mat/microstructure-segmentation-benchmark
+
+## Fine-Tuning Routes and Checkpoints
+
+| Dataset | Route | Classes | File |
+| --- | --- | ---: | --- |
+| Aachen-Heerlen | LoRA plus mask decoder | 2 | `checkpoints/Aachen-Heerlen/microsam_lora_decoder_best.pth` |
+| EMPS | LoRA plus mask decoder | 2 | `checkpoints/EMPS/microsam_lora_decoder_best.pth` |
+| Grain | LoRA plus mask decoder | 2 | `checkpoints/Grain/microsam_lora_decoder_best.pth` |
+| EBC | LoRA, mask decoder, and class tokens | 3 | `checkpoints/EBC/semantic_microsam_best.pth` |
+| Super | LoRA, mask decoder, and class tokens | 3 | `checkpoints/Super/semantic_microsam_best.pth` |
+| MetalDAM | LoRA, mask decoder, and class tokens | 5 | `checkpoints/MetalDAM/semantic_microsam_best.pth` |
+| UHCS | LoRA, mask decoder, and class tokens | 7 | `checkpoints/UHCS/semantic_microsam_best.pth` |
+
+Class counts include background.
+
+## Base Model Requirement
+
+Initialize the required base model with:
+
+```bash
+python -c "from micro_sam.util import get_sam_model; get_sam_model(model_type='vit_b_lm')"
+```
+
+The corresponding BioImage.IO checkpoint is available at:
+https://uk1s3.embassy.ebi.ac.uk/public-datasets/bioimage.io/diplomatic-bug/1.2/files/vit_b.pt
+
+## Training Details
+
+- Base architecture: MicroSAM ViT-B-LM
+- SAM encoder input: longest side resized and padded to 1024 x 1024
+- Training batch size: 1
+- Epochs: 200
+- Optimizer: AdamW
+- Learning rate: 0.0001
+- Weight decay: 0.0001
+- Scheduler: CosineAnnealingLR with minimum learning rate 0.000001
+- Early-stopping patience: 50
+- LoRA rank: 8
+- LoRA alpha: 16
+- LoRA dropout: 0.05
+- LoRA targets: `qkv` and `proj`
+- Binary objective: BCE, Dice, and IoU MSE
+- Multiclass objective: cross-entropy and Dice
+
+## Usage
+
+Example for binary EMPS segmentation:
+
+```bash
+hf download NAMESPACE/microstructure-micro-sam checkpoints/EMPS/microsam_lora_decoder_best.pth --local-dir weights/micro-sam
+python microsam/test_lora_decoder.py \
+  --dataset-root /path/to/EMPS \
+  --finetuned-checkpoint weights/micro-sam/checkpoints/EMPS/microsam_lora_decoder_best.pth \
+  --output-dir results/micro_sam_emps
+```
+
+For multiclass checkpoints, use `microsam/test_semantic_microsam.py` and pass
+the class count from the table.
+
+## Inference Parameters
+
+Automatic instance segmentation uses `min_size=10`,
+`center_distance_threshold=0.5`, and `boundary_distance_threshold=0.5`.
+
+## Evaluation, Intended Use, and Limitations
+
+Evaluation saves original-resolution masks and calculates the common benchmark
+metrics through `Myutils/metrics.py`. These checkpoints require the matching
+MicroSAM `vit_b_lm` base model and source environment. MicroSAM dependencies
+such as `vigra` and `python-elf` are most reliably installed through the
+official Conda environment on Linux. The checkpoints are intended for research
+reproduction and are not validated for safety-critical or industrial
+quality-control decisions.
+
+## License and Citation
+
+The released experiment files are provided under the MIT license. MicroSAM and
+its base model remain subject to upstream terms and the base checkpoint is not
+redistributed here. A paper citation will be added after publication.

checkpoint_manifest.json

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+{
+  "model": "MicroSAM ViT-B-LM",
+  "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": "lora_and_mask_decoder",
+      "path": "checkpoints/Aachen-Heerlen/microsam_lora_decoder_best.pth",
+      "size_bytes": 18113211,
+      "sha256": "cdb97adbc50608525962d6bf39803c9e13e6d87f5589296db53a86b3765219a2",
+      "requires_base_checkpoint": "vit_b_lm"
+    },
+    {
+      "dataset": "EBC",
+      "task": "multiclass_semantic_segmentation",
+      "num_classes": 3,
+      "fine_tuning_route": "lora_mask_decoder_and_class_tokens",
+      "path": "checkpoints/EBC/semantic_microsam_best.pth",
+      "size_bytes": 18117576,
+      "sha256": "a9901554236cbde55fb7bba896e99347a4dd6a15e26e19df0f64fc1fd198f37d",
+      "requires_base_checkpoint": "vit_b_lm"
+    },
+    {
+      "dataset": "EMPS",
+      "task": "binary_segmentation",
+      "num_classes": 2,
+      "fine_tuning_route": "lora_and_mask_decoder",
+      "path": "checkpoints/EMPS/microsam_lora_decoder_best.pth",
+      "size_bytes": 18113211,
+      "sha256": "acd96d5a026a3fd3288b191ee92692a22860a0d05b50458457ddf162239daa87",
+      "requires_base_checkpoint": "vit_b_lm"
+    },
+    {
+      "dataset": "Grain",
+      "task": "binary_segmentation",
+      "num_classes": 2,
+      "fine_tuning_route": "lora_and_mask_decoder",
+      "path": "checkpoints/Grain/microsam_lora_decoder_best.pth",
+      "size_bytes": 18113211,
+      "sha256": "69f7e3ce2aa76e79bb87818483c33a18bd5a353990f86bcfe77c8e5b94ab3c23",
+      "requires_base_checkpoint": "vit_b_lm"
+    },
+    {
+      "dataset": "MetalDAM",
+      "task": "multiclass_semantic_segmentation",
+      "num_classes": 5,
+      "fine_tuning_route": "lora_mask_decoder_and_class_tokens",
+      "path": "checkpoints/MetalDAM/semantic_microsam_best.pth",
+      "size_bytes": 18119624,
+      "sha256": "965f1d87cbbb644301595135e4d83726b2242277a68fcea80f433f0888c95193",
+      "requires_base_checkpoint": "vit_b_lm"
+    },
+    {
+      "dataset": "Super",
+      "task": "multiclass_semantic_segmentation",
+      "num_classes": 3,
+      "fine_tuning_route": "lora_mask_decoder_and_class_tokens",
+      "path": "checkpoints/Super/semantic_microsam_best.pth",
+      "size_bytes": 18117576,
+      "sha256": "c55d2f3df5534b56b49584f6de879c782a81a4096273ccca4409e16c6136c258",
+      "requires_base_checkpoint": "vit_b_lm"
+    },
+    {
+      "dataset": "UHCS",
+      "task": "multiclass_semantic_segmentation",
+      "num_classes": 7,
+      "fine_tuning_route": "lora_mask_decoder_and_class_tokens",
+      "path": "checkpoints/UHCS/semantic_microsam_best.pth",
+      "size_bytes": 18121672,
+      "sha256": "dc541c823ea9f55bc932c6c1beb5b58d7341f545dffb78ff0e2dc6d388fae4eb",
+      "requires_base_checkpoint": "vit_b_lm"
+    }
+  ]
+}

checkpoints/Aachen-Heerlen/microsam_lora_decoder_best.pth

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