segment-anything-model

Zero-shot image segmentation on any object is performed by providing flexible prompts like points, boxes, or previous masks to generate accurate masks without manual annotation.

Automating mask generation for image annotation pipelines is best handled using automatic mask generation features alongside SAM backbones to reduce manual effort and speed up prototyping.

You can use the Segment Anything Model with PyTorch and optional ONNX support to deploy cross-platform inference workflows across diverse domains and environments.

Choosing a ViT backbone for zero-shot segmentation tasks depends on your requirements: select ViT-B for speed, or ViT-L and ViT-H models when prioritizing accuracy.

You need to install the Segment Anything Model package and PyTorch to load a backbone and begin generating image masks from input prompts like points and boxes.

Zero-shot segmentation with prompts works by utilizing points, boxes, or previous masks to identify and isolate any object in an image across diverse domains without requiring domain-specific training.