segment-anything-model

Image segmentation with a click or bounding box prompt converts your input coordinates into precise binary masks. You provide the prompt over the target object, and the model isolates its boundaries without requiring custom training.

Automatic mask generation produces all object masks in an image for dataset creation or annotation. The model scans the image and outputs multiple binary masks, enabling downstream vision pipelines without manual tracing.

ONNX export supports deploying image segmentation models in production web services. This workflow allows you to apply interactive segmentation and object extraction efficiently within web applications.

The best way to extract a product is using prompt-based image segmentation. You apply a point or box prompt over the product, refine boundaries with background points, and export the resulting binary mask for editing.

Reusing image embeddings speeds up interactive annotation across multiple prompts. The model computes the embedding once, allowing deterministic and efficient mask generation for subsequent point or box prompts.

Zero-shot image segmentation works for medical imaging without training a custom model. You can apply interactive prompts directly to medical scans to isolate anatomical structures and extract precise binary masks.