multimodal-segment-anything

原文涵盖的主题

• Segment Anything Model (SAM)
• When to use SAM
• Quick start
• Installation
• Download checkpoints
• Basic usage with SamPredictor
• HuggingFace Transformers
• Core concepts

Comprehensive guide to using Meta AI's Segment Anything Model for zero-shot image segmentation. 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 | Model | Checkpoint | Size | Speed | Accuracy | |-------|------------|------|-------|----------| | ViT-H | vith | 2.4 GB | Slowest

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