Segmentation Methods for Quantitative Microstructure Analysi
Collection
Fine-tuned checkpoints for eight microstructure segmentation models evaluated on seven datasets. • 8 items • Updated
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/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/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:
qkvandproj - Binary objective: BCE, Dice, and IoU MSE
- Multiclass objective: cross-entropy and Dice
Usage
Example for binary EMPS segmentation:
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, box_nms_thresh=0.7, 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.