segment-anything

Zero-shot image segmentation generates accurate masks by accepting prompts like points, boxes, or previous masks to guide the model without task-specific training. This approach produces prompt-driven masks for any object across diverse visual domains.

To extract objects from images, you provide an input image alongside specific prompts like spatial points or bounding boxes. The model processes these prompts to generate accurate segmentation masks for the requested regions, optionally outputting multiple candidate masks.

Yes, you need a Python environment with PyTorch installed to run a compatible Segment Anything Model checkpoint. The environment must support processing prompts via points, boxes, or masks to execute the zero-shot image segmentation workflow.

Yes, you can export the image segmentation model to ONNX for production deployment in different environments. This ONNX support enables advanced workflows including video batch processing and integration into production AI agent systems.

The best way to perform multi-object segmentation is by using automatic mask generation to output multiple candidate masks, selecting the best by score. This prompt-based approach handles multi-object segmentation in single images or videos.