segment-anything-model

Zero-shot image segmentation uses interactive prompting with points, bounding boxes, or previous masks to generate accurate masks for arbitrary images. You load a SAM checkpoint into SamPredictor, set the image, and call predict with foreground prompts to retrieve the highest-scoring mask.

Interactive prompting in segment anything model workflows involves providing points, bounding boxes, or previous masks to guide mask generation. This click-based workflow allows targeted segmentation of specific objects without requiring task-specific model training or predefined category labels.

Yes, segment anything model supports ONNX deployment workflows. The Skill provides export paths and guidance for ONNX runtime execution, plus troubleshooting for common runtime issues encountered during inference deployment.

SAM inference requires PyTorch version 1.7.0 or higher and transformers version 4.30.0 or higher. You also need a valid SAM checkpoint file and compatible dependencies including opencv-python, pycocotools, matplotlib, and onnxruntime for full functionality.

Yes, segment anything model supports automatic mask generation alongside interactive modes. Automatic mode generates all candidate masks for an entire image without requiring user prompts, enabling batch dataset creation and comprehensive image understanding tasks.

Use zero-shot segmentation when object categories are unknown in advance or when you need masks across new image domains without fine-tuning. It suits interactive annotation, dataset creation, and downstream pipelines where training data is unavailable or labeling speed matters.