segment-anything-model

Zero-shot image segmentation isolates objects without task-specific training by accepting point, box, or mask prompts. You load a pretrained checkpoint and pass prompts to generate accurate masks instantly across diverse domains.

Automatic mask generation segments every object in an image without manual prompts. The SAM architecture processes the entire image through its encoder and decoder to identify and extract all distinct object masks automatically.

Zero-shot segmentation with SAM requires torch>=1.7.0 and transformers>=4.30.0, plus the segment-anything package. Ensure these dependencies are installed in your Python environment before loading model checkpoints.

The best way to build an annotation tool is using zero-shot segmentation to reduce manual labeling effort. SAM supports interactive point and box prompts, enabling rapid dataset creation across medical, satellite, and general imagery domains.

You can export the SAM image segmentation model to ONNX format for deployment. This allows the zero-shot segmentation workflow to run efficiently in production environments outside the original PyTorch framework.