segment-anything-modellisted

# Segment Anything Model (SAM) Comprehensive guide to using Meta AI's Segment Anything Model for zero-shot image segmentation. ## When to use SAM **Use SAM when:** - Need to segment any object in images without task-specific training - Building interactive annotation tools with point/box prompts - Generating training data for other vision models - Need zero-shot transfer to new image domains - Building object detection/segmentation pipelines - Processing medical, satellite, or domain-specific images **Key features:** - **Zero-shot segmentation**: Works on any image domain without fine-tuning - **Flexible prompts**: Points, bounding boxes, or previous masks - **Automatic segmentation**: Generate all object masks automatically - **High quality**: Trained on 1.1 billion masks from 11 million images - **Multiple model sizes**: ViT-B (fastest), ViT-L, ViT-H (most accurate) - **ONNX export**: Deploy in browsers and edge devices **Use alternatives instead:** - **YOLO/Detectron2**: For real-time object detection with classes - **Mask2Former**: For semantic/panoptic segmentation with categories - **GroundingDINO + SAM**: For text-prompted segmentation - **SAM 2**: For video segmentation tasks ## Quick start ### Installation ```bash # From GitHub pip install git+https://github.com/facebookresearch/segment-anything.git # Optional dependencies pip install opencv-python pycocotools matplotlib # Or use HuggingFace transformers pip install transformers ``` ### Download checkpoint