| --- |
| 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/Segmentation-methods-evaluation-for-quantitative-microstructure-analysis |
|
|
| ## 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. |
|
|
