Upload model card and dataset checkpoints

CHECKSUMS.sha256

@@ -0,0 +1,7 @@
+da7ffbb7dcf2fa0ab7278fb88ed3654e90d1cef2fcba00b18b977ae4c5ced313  checkpoints/Aachen-Heerlen/sam2_lora_decoder_best.pth
+33a37f95eb3d50485038da3e0dc8494f469bd11c5bd4e3c09ccd10956f150524  checkpoints/EBC/semantic_sam2_best.pth
+3551a19ecc984fb999cdb7b1475ec258776e0dfdebdd4caedb91ad102df6eb39  checkpoints/EMPS/sam2_lora_decoder_best.pth
+7bb0e3036b685eddb1c9b6d4ce3f62c3ce33d4cad971f9d6ae4913601566d37e  checkpoints/Grain/sam2_lora_decoder_best.pth
+5031b5040d1f05d65edd4b53ef8cddd135d7848ff57bab7db3ea2b6a90de7f0b  checkpoints/MetalDAM/semantic_sam2_best.pth
+d9b8fdc275efa1c58c40287777a7d2c72c0c4c9a66f71439fb946c4acfacac1d  checkpoints/Super/semantic_sam2_best.pth
+9188cfc3d8ffe9aeefa26b65129223726d2fa9742cd343b94578291e260bdd64  checkpoints/UHCS/semantic_sam2_best.pth

LICENSE

@@ -0,0 +1,21 @@
+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

@@ -0,0 +1,101 @@
+---
+license: mit
+library_name: pytorch
+pipeline_tag: image-segmentation
+tags:
+- semantic-segmentation
+- microstructure
+- materials-science
+- segment-anything-2
+- lora
+- sam2
+---
+
+# SAM2 Fine-Tuned Checkpoints for Microstructure Segmentation
+
+## Model Description
+
+This repository contains dataset-specific fine-tuned parameter checkpoints for
+SAM2.1 Hiera Base+. The files are partial checkpoints and must be loaded with
+the exact `sam2.1_hiera_base_plus.pt` base checkpoint and
+`configs/sam2.1/sam2.1_hiera_b+.yaml`.
+
+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/sam2_lora_decoder_best.pth` |
+| EMPS | LoRA plus mask decoder | 2 | `checkpoints/EMPS/sam2_lora_decoder_best.pth` |
+| Grain | LoRA plus mask decoder | 2 | `checkpoints/Grain/sam2_lora_decoder_best.pth` |
+| EBC | LoRA, mask decoder, and class tokens | 3 | `checkpoints/EBC/semantic_sam2_best.pth` |
+| Super | LoRA, mask decoder, and class tokens | 3 | `checkpoints/Super/semantic_sam2_best.pth` |
+| MetalDAM | LoRA, mask decoder, and class tokens | 5 | `checkpoints/MetalDAM/semantic_sam2_best.pth` |
+| UHCS | LoRA, mask decoder, and class tokens | 7 | `checkpoints/UHCS/semantic_sam2_best.pth` |
+
+Class counts include background.
+
+## Base Model Requirement
+
+Download the official SAM2.1 Hiera Base+ checkpoint from:
+https://dl.fbaipublicfiles.com/segment_anything_2/092824/sam2.1_hiera_base_plus.pt
+
+Do not substitute another SAM2 architecture without changing the model config.
+
+## Training Details
+
+- Base architecture: SAM2.1 Hiera Base+
+- Input preprocessing: longest side resized to 1024, 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-sam2 checkpoints/EMPS/sam2_lora_decoder_best.pth --local-dir weights/sam2
+python sam2/test_lora_decoder.py \
+  --dataset-root /path/to/EMPS \
+  --checkpoint weights/pretrained/sam2.1_hiera_base_plus.pt \
+  --finetuned-checkpoint weights/sam2/checkpoints/EMPS/sam2_lora_decoder_best.pth \
+  --output-dir results/sam2_emps
+```
+
+For multiclass checkpoints, use `sam2/test_semantic_sam2.py` and pass the
+class count from the table.
+
+## Inference Parameters
+
+Automatic mask generation uses `points_per_side=32`, `points_per_batch=64`,
+`pred_iou_thresh=0.8`, `stability_score_thresh=0.8`, and `crop_n_layers=0`.
+Padded regions are removed before predictions are restored to the original
+image size.
+
+## Evaluation, Intended Use, and Limitations
+
+Evaluation saves original-resolution masks and calculates the common benchmark
+metrics through `Myutils/metrics.py`. These checkpoints require the exact
+SAM2.1 Hiera Base+ base model and matching source code. They are intended for
+research reproduction 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. SAM2 and its
+base checkpoint remain subject to Meta's upstream terms and are not
+redistributed here. A paper citation will be added after publication.

checkpoint_manifest.json

@@ -0,0 +1,77 @@
+{
+  "model": "SAM2.1 Hiera Base+",
+  "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/sam2_lora_decoder_best.pth",
+      "size_bytes": 19068232,
+      "sha256": "da7ffbb7dcf2fa0ab7278fb88ed3654e90d1cef2fcba00b18b977ae4c5ced313",
+      "requires_base_checkpoint": "sam2.1_hiera_base_plus.pt"
+    },
+    {
+      "dataset": "EBC",
+      "task": "multiclass_semantic_segmentation",
+      "num_classes": 3,
+      "fine_tuning_route": "lora_mask_decoder_and_class_tokens",
+      "path": "checkpoints/EBC/semantic_sam2_best.pth",
+      "size_bytes": 19072973,
+      "sha256": "33a37f95eb3d50485038da3e0dc8494f469bd11c5bd4e3c09ccd10956f150524",
+      "requires_base_checkpoint": "sam2.1_hiera_base_plus.pt"
+    },
+    {
+      "dataset": "EMPS",
+      "task": "binary_segmentation",
+      "num_classes": 2,
+      "fine_tuning_route": "lora_and_mask_decoder",
+      "path": "checkpoints/EMPS/sam2_lora_decoder_best.pth",
+      "size_bytes": 19068232,
+      "sha256": "3551a19ecc984fb999cdb7b1475ec258776e0dfdebdd4caedb91ad102df6eb39",
+      "requires_base_checkpoint": "sam2.1_hiera_base_plus.pt"
+    },
+    {
+      "dataset": "Grain",
+      "task": "binary_segmentation",
+      "num_classes": 2,
+      "fine_tuning_route": "lora_and_mask_decoder",
+      "path": "checkpoints/Grain/sam2_lora_decoder_best.pth",
+      "size_bytes": 19068232,
+      "sha256": "7bb0e3036b685eddb1c9b6d4ce3f62c3ce33d4cad971f9d6ae4913601566d37e",
+      "requires_base_checkpoint": "sam2.1_hiera_base_plus.pt"
+    },
+    {
+      "dataset": "MetalDAM",
+      "task": "multiclass_semantic_segmentation",
+      "num_classes": 5,
+      "fine_tuning_route": "lora_mask_decoder_and_class_tokens",
+      "path": "checkpoints/MetalDAM/semantic_sam2_best.pth",
+      "size_bytes": 19075021,
+      "sha256": "5031b5040d1f05d65edd4b53ef8cddd135d7848ff57bab7db3ea2b6a90de7f0b",
+      "requires_base_checkpoint": "sam2.1_hiera_base_plus.pt"
+    },
+    {
+      "dataset": "Super",
+      "task": "multiclass_semantic_segmentation",
+      "num_classes": 3,
+      "fine_tuning_route": "lora_mask_decoder_and_class_tokens",
+      "path": "checkpoints/Super/semantic_sam2_best.pth",
+      "size_bytes": 19072973,
+      "sha256": "d9b8fdc275efa1c58c40287777a7d2c72c0c4c9a66f71439fb946c4acfacac1d",
+      "requires_base_checkpoint": "sam2.1_hiera_base_plus.pt"
+    },
+    {
+      "dataset": "UHCS",
+      "task": "multiclass_semantic_segmentation",
+      "num_classes": 7,
+      "fine_tuning_route": "lora_mask_decoder_and_class_tokens",
+      "path": "checkpoints/UHCS/semantic_sam2_best.pth",
+      "size_bytes": 19077069,
+      "sha256": "9188cfc3d8ffe9aeefa26b65129223726d2fa9742cd343b94578291e260bdd64",
+      "requires_base_checkpoint": "sam2.1_hiera_base_plus.pt"
+    }
+  ]
+}

checkpoints/Aachen-Heerlen/sam2_lora_decoder_best.pth

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:da7ffbb7dcf2fa0ab7278fb88ed3654e90d1cef2fcba00b18b977ae4c5ced313
+size 19068232

checkpoints/EBC/semantic_sam2_best.pth

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:33a37f95eb3d50485038da3e0dc8494f469bd11c5bd4e3c09ccd10956f150524
+size 19072973

checkpoints/EMPS/sam2_lora_decoder_best.pth

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:3551a19ecc984fb999cdb7b1475ec258776e0dfdebdd4caedb91ad102df6eb39
+size 19068232

checkpoints/Grain/sam2_lora_decoder_best.pth

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:7bb0e3036b685eddb1c9b6d4ce3f62c3ce33d4cad971f9d6ae4913601566d37e
+size 19068232

checkpoints/MetalDAM/semantic_sam2_best.pth

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:5031b5040d1f05d65edd4b53ef8cddd135d7848ff57bab7db3ea2b6a90de7f0b
+size 19075021

checkpoints/Super/semantic_sam2_best.pth

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:d9b8fdc275efa1c58c40287777a7d2c72c0c4c9a66f71439fb946c4acfacac1d
+size 19072973

checkpoints/UHCS/semantic_sam2_best.pth

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:9188cfc3d8ffe9aeefa26b65129223726d2fa9742cd343b94578291e260bdd64
+size 19077069