segment-anything-model

Zero-shot segmentation automates object detection and segmentation in images without specific training. The Segment Anything Model generates high-quality masks for any object using point or box prompts, trained on 1.1 billion masks for accuracy.

To generate image masks with SAM, provide point coordinates or bounding boxes as prompts. Load your target image and pass the prompt coordinates to the model to instantly segment the specified object without manual masking.

Yes, SAM requires PyTorch (>=1.7.0) and Transformers (>=4.30.0) libraries for model inference. You also need the segment-anything package installed to execute the zero-shot segmentation workflows.

Yes, SAM supports ONNX export for edge device deployment. This allows you to deploy the trained image segmentation models in browsers and edge devices for flexible zero-shot inference environments.

SAM offers three model sizes: ViT-B, ViT-L, and ViT-H. You can choose between fast and accurate model sizes based on your image segmentation requirements and available computational resources.

Automatic segmentation generates all object masks in an image without requiring prompts. Use the Segment Anything Model to automatically detect and segment every object, suitable for image processing workflows and data extraction.