tianrun-chen/SAM-Adapter-PyTorch

Health v2 · maintenance only

Flags: no_releases

Adoption not part of the score

1551 stars · 123 forks observed · 2026-08-28

What it is AI-extracted, prompt v1, taxonomy v1, 2026-08-30, confidence not recorded

A PyTorch library that adapts Meta AI's Segment Anything Model (SAM, SAM2, SAM3) to underperforming downstream segmentation tasks using lightweight adapters and prompts. It provides training and evaluation pipelines for tasks like camouflaged object detection, shadow detection, and medical image segmentation.

Use cases

• fine-tune segment anything model on my own dataset
• segment camouflaged objects in images
• adapt SAM for medical image segmentation
• polyp segmentation from endoscopy images
• shadow detection with segment anything
• train SAM adapter with distributed training on multiple GPUs
• use SAM2 or SAM3 backbone for downstream segmentation

When to choose

• you need to adapt SAM/SAM2/SAM3 to a specialized segmentation domain where the base model underperforms
• you want a lightweight adapter-based fine-tuning approach instead of full model retraining
• you work on camouflaged object detection, shadow removal, or medical segmentation tasks

When to avoid

• you only need general-purpose promptable segmentation without domain adaptation
• you lack access to high-memory GPUs, since training requires multiple large-memory cards
• you need a production-ready inference service rather than a research codebase

Facets

library · maturity active

machine-learning deep-learning image-processing computer-vision computer-vision image-processing machine-learning deep-learning python segment-anything sam-adapter fine-tuning image-segmentation camouflaged-object-detection medical-imaging pytorch iccv-2023 gpu

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markdown · JSON · MCP: product_card(name="tianrun-chen/SAM-Adapter-PyTorch")