Upload model card and dataset checkpoints

CHECKSUMS.sha256

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+4bbca68e4f700cc635e2cbf81b2c92f5ba67ab0ba635646f9eae249ee4da79e9  checkpoints/Aachen-Heerlen/hqsam_lora_decoder_best.pth
+edcd912db9c947d98c2a17915a8d4c6c34aa6b3e00f73ec4731ed34ce0a1cd9f  checkpoints/EBC/semantic_hqsam_best.pth
+bf2c21e39bc3f0fb1b90475fd5e56b7991da125704c99aaacd3f4a8e50c35efd  checkpoints/EMPS/hqsam_lora_decoder_best.pth
+fb1063891bed2c39ae8688e42f1f1717e6e0a8a2c578c1129bed127ebb620bfa  checkpoints/Grain/hqsam_lora_decoder_best.pth
+1eb8e3822d478f41672e115fc3bb7512ce5dd894a792bcd0d75d0abb76a7718f  checkpoints/MetalDAM/semantic_hqsam_best.pth
+b673cea3e2b0f06cc05f538510f29b4f267d478de7625b4079aeea4780d80ae7  checkpoints/Super/semantic_hqsam_best.pth
+93b6cd6aa651cd03cafcf8a2c291aaa226dbc505daaee92a30b9c287735ac18b  checkpoints/UHCS/semantic_hqsam_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
+- materials-science
+- segment-anything
+- lora
+- hq-sam
+---
+
+# HQ-SAM Fine-Tuned Checkpoints for Microstructure Segmentation
+
+## Model Description
+
+This repository contains dataset-specific fine-tuned parameter checkpoints for
+HQ-SAM with its ViT-B backbone. The files do not contain the full base model.
+Load them together with `sam_hq_vit_b.pth` using the matching source script.
+
+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/hqsam_lora_decoder_best.pth` |
+| EMPS | LoRA plus mask decoder | 2 | `checkpoints/EMPS/hqsam_lora_decoder_best.pth` |
+| Grain | LoRA plus mask decoder | 2 | `checkpoints/Grain/hqsam_lora_decoder_best.pth` |
+| EBC | LoRA, mask decoder, and class tokens | 3 | `checkpoints/EBC/semantic_hqsam_best.pth` |
+| Super | LoRA, mask decoder, and class tokens | 3 | `checkpoints/Super/semantic_hqsam_best.pth` |
+| MetalDAM | LoRA, mask decoder, and class tokens | 5 | `checkpoints/MetalDAM/semantic_hqsam_best.pth` |
+| UHCS | LoRA, mask decoder, and class tokens | 7 | `checkpoints/UHCS/semantic_hqsam_best.pth` |
+
+Class counts include background.
+
+## Base Model Requirement
+
+Download the official HQ-SAM ViT-B checkpoint from:
+https://huggingface.co/lkeab/hq-sam/resolve/main/sam_hq_vit_b.pth
+
+The files in this repository contain LoRA and mask-decoder parameters.
+Multiclass files additionally contain learned class tokens.
+
+## Training Details
+
+- Base architecture: HQ-SAM ViT-B
+- 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-hq-sam checkpoints/EMPS/hqsam_lora_decoder_best.pth --local-dir weights/hq-sam
+python hqsam/test_lora_decoder.py \
+  --dataset-root /path/to/EMPS \
+  --checkpoint weights/pretrained/sam_hq_vit_b.pth \
+  --finetuned-checkpoint weights/hq-sam/checkpoints/EMPS/hqsam_lora_decoder_best.pth \
+  --output-dir results/hq_sam_emps
+```
+
+For multiclass checkpoints, use `hqsam/test_semantic_hqsam.py` and pass the
+class count from the table.
+
+## Inference Parameters
+
+The original automatic-mask path uses `points_per_side=32`,
+`pred_iou_thresh=0.85`, `stability_score_thresh=0.8`, and `crop_n_layers=0`.
+The binary fine-tuned path uses `pred_iou_thresh=0.78` and
+`stability_score_thresh=0.8`.
+
+## Evaluation, Intended Use, and Limitations
+
+Evaluation saves original-resolution masks and calculates the common benchmark
+metrics through `Myutils/metrics.py`. These checkpoints are intended for
+research reproduction on the named datasets. They require the matching HQ-SAM
+ViT-B base checkpoint and may not generalize to unseen materials or imaging
+conditions. 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. HQ-SAM and
+its base checkpoint remain subject to upstream terms and are not redistributed
+here. A paper citation will be added after publication.

checkpoint_manifest.json

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+{
+  "model": "HQ-SAM",
+  "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/hqsam_lora_decoder_best.pth",
+      "size_bytes": 22405385,
+      "sha256": "4bbca68e4f700cc635e2cbf81b2c92f5ba67ab0ba635646f9eae249ee4da79e9",
+      "requires_base_checkpoint": "sam_hq_vit_b.pth"
+    },
+    {
+      "dataset": "EBC",
+      "task": "multiclass_semantic_segmentation",
+      "num_classes": 3,
+      "fine_tuning_route": "lora_mask_decoder_and_class_tokens",
+      "path": "checkpoints/EBC/semantic_hqsam_best.pth",
+      "size_bytes": 22409839,
+      "sha256": "edcd912db9c947d98c2a17915a8d4c6c34aa6b3e00f73ec4731ed34ce0a1cd9f",
+      "requires_base_checkpoint": "sam_hq_vit_b.pth"
+    },
+    {
+      "dataset": "EMPS",
+      "task": "binary_segmentation",
+      "num_classes": 2,
+      "fine_tuning_route": "lora_and_mask_decoder",
+      "path": "checkpoints/EMPS/hqsam_lora_decoder_best.pth",
+      "size_bytes": 22405385,
+      "sha256": "bf2c21e39bc3f0fb1b90475fd5e56b7991da125704c99aaacd3f4a8e50c35efd",
+      "requires_base_checkpoint": "sam_hq_vit_b.pth"
+    },
+    {
+      "dataset": "Grain",
+      "task": "binary_segmentation",
+      "num_classes": 2,
+      "fine_tuning_route": "lora_and_mask_decoder",
+      "path": "checkpoints/Grain/hqsam_lora_decoder_best.pth",
+      "size_bytes": 22405385,
+      "sha256": "fb1063891bed2c39ae8688e42f1f1717e6e0a8a2c578c1129bed127ebb620bfa",
+      "requires_base_checkpoint": "sam_hq_vit_b.pth"
+    },
+    {
+      "dataset": "MetalDAM",
+      "task": "multiclass_semantic_segmentation",
+      "num_classes": 5,
+      "fine_tuning_route": "lora_mask_decoder_and_class_tokens",
+      "path": "checkpoints/MetalDAM/semantic_hqsam_best.pth",
+      "size_bytes": 22411887,
+      "sha256": "1eb8e3822d478f41672e115fc3bb7512ce5dd894a792bcd0d75d0abb76a7718f",
+      "requires_base_checkpoint": "sam_hq_vit_b.pth"
+    },
+    {
+      "dataset": "Super",
+      "task": "multiclass_semantic_segmentation",
+      "num_classes": 3,
+      "fine_tuning_route": "lora_mask_decoder_and_class_tokens",
+      "path": "checkpoints/Super/semantic_hqsam_best.pth",
+      "size_bytes": 22409839,
+      "sha256": "b673cea3e2b0f06cc05f538510f29b4f267d478de7625b4079aeea4780d80ae7",
+      "requires_base_checkpoint": "sam_hq_vit_b.pth"
+    },
+    {
+      "dataset": "UHCS",
+      "task": "multiclass_semantic_segmentation",
+      "num_classes": 7,
+      "fine_tuning_route": "lora_mask_decoder_and_class_tokens",
+      "path": "checkpoints/UHCS/semantic_hqsam_best.pth",
+      "size_bytes": 22413935,
+      "sha256": "93b6cd6aa651cd03cafcf8a2c291aaa226dbc505daaee92a30b9c287735ac18b",
+      "requires_base_checkpoint": "sam_hq_vit_b.pth"
+    }
+  ]
+}

checkpoints/Aachen-Heerlen/hqsam_lora_decoder_best.pth

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