segment-anything-model

Zero-shot segmentation generates object masks across any image domain without fine-tuning by accepting prompts such as points, bounding boxes, or previous masks to produce high-quality outputs for dataset curation.

Image segmentation with SAM accepts flexible prompts including points, boxes, or previous masks to produce high-quality object masks, enabling interactive annotation without requiring task-specific training.

Automating interactive annotation is best achieved using zero-shot segmentation to automatically generate multiple high-quality object masks across diverse image types, accelerating dataset creation.

Zero-shot segmentation supports ONNX export for deployment and offers multiple ViT backbones (ViT-B/L/H) to accommodate different scale and performance requirements.

Zero-shot segmentation can be applied to medical and satellite imagery analysis without fine-tuning, operating across diverse image types to extract objects and prepare data.

Zero-shot segmentation outputs masks with associated scores and metadata, providing confidence metrics alongside the generated object masks for quality assessment.