| --- |
| license: apache-2.0 |
| language: |
| - en |
| pipeline_tag: image-segmentation |
| tags: |
| - pytorch |
| - sam2 |
| - microcrack-segmentation |
| - video-segmentation |
| --- |
| |
| # MP-SAM2 |
|
|
| Fine-tuned models for **A Multi-Prompt Transfer Learning Framework Based on |
| Segment Anything Model 2 for Microcrack Segmentation in Engineered Cementitious |
| Composite Images and Videos**. |
|
|
| [Source and installation](https://github.com/cclyr/MP-SAM2) · |
| [Training settings](https://github.com/cclyr/MP-SAM2/tree/main/configs) · |
| [Model architecture](https://github.com/cclyr/MP-SAM2/blob/main/sam2/configs/sam2_hiera_l.yaml) |
|
|
| MP-SAM2 combines an automatic threshold mask, SP-Box, and EDT positive/negative |
| point prompts with progressive SAM2 transfer learning. The GitHub repository |
| provides training, image inference, video propagation, and evaluation scripts. |
|
|
| ## Checkpoints |
|
|
| | File | Training images | |
| |---|---:| |
| | [mp_sam2_5.torch](https://huggingface.co/ccllr/MP-SAM2/resolve/main/mp_sam2_5.torch) | 5 | |
| | [mp_sam2_10.torch](https://huggingface.co/ccllr/MP-SAM2/resolve/main/mp_sam2_10.torch) | 10 | |
| | [mp_sam2_50.torch](https://huggingface.co/ccllr/MP-SAM2/resolve/main/mp_sam2_50.torch) | 50 | |
| | [mp_sam2_full.torch](https://huggingface.co/ccllr/MP-SAM2/resolve/main/mp_sam2_full.torch) | 186 (full data) | |
|
|
| Each file is approximately 898 MB and contains the complete model state for the |
| bundled `sam2_hiera_l.yaml` architecture. Each file is an individual trained |
| model; the paper's few-shot results summarize repeated training runs. |
| A 20-image checkpoint is not included. `SHA256SUMS` lists the file hashes. |
|
|
| ## Usage |
|
|
| Follow the [source installation instructions](https://github.com/cclyr/MP-SAM2#installation). |
| From the source repository root: |
|
|
| ```bash |
| python scripts/download_checkpoints.py --variant full |
| |
| python scripts/infer_image.py \ |
| --input path/to/image.png \ |
| --checkpoint checkpoints/mp_sam2_full.torch \ |
| --config configs/prompt.yaml \ |
| --output outputs/image |
| ``` |
|
|
| Use `--variant 5`, `10`, `50`, or `all` for other checkpoints. Direct downloads |
| are also linked above. The download helper verifies MP-SAM2 file checksums. |
|
|
| Training and video inference also need Meta's original |
| [SAM2.1 Hiera Large checkpoint](https://dl.fbaipublicfiles.com/segment_anything_2/092824/sam2.1_hiera_large.pt). |
| Download it into `checkpoints/` with: |
|
|
| ```bash |
| python scripts/download_checkpoints.py --variant full --include-base |
| ``` |
|
|
| The transfer loader selects matching SAM2.1 tensors for the bundled MP-SAM2 |
| architecture, excluding SAM2.1-specific spatial and pointer-projection heads. |
| Use this repository's runtime and configuration with these weights. The files |
| are custom PyTorch state dictionaries loaded by the MP-SAM2 scripts; a |
| Transformers `AutoModel` interface is not supplied. |
|
|
| `configs/prompt.yaml` controls automatic prompts for image inference and the |
| first video frame. `configs/fewshot.yaml` and `configs/full.yaml` control the |
| respective training schedules. Full instructions, data layout, and evaluation |
| commands are in the [README](https://github.com/cclyr/MP-SAM2#readme). |
|
|
| ## Scope and license |
|
|
| The intended application is microcrack segmentation in engineered cementitious |
| composite images and laboratory videos. |
| The original Meta checkpoint is obtained from upstream. |
|
|
| The four fine-tuned files are distributed under Apache License 2.0. See `LICENSE` |
| and the upstream SAM2 attribution in `NOTICE`. Citation metadata is provided in |
| the source repository's [CITATION.cff](https://github.com/cclyr/MP-SAM2/blob/main/CITATION.cff). |
|
|
