segment-anything-model

Zero-shot image segmentation masks are generated by prompting the Segment Anything Model with points, boxes, or previous masks to isolate objects across diverse domains like photos, medical, and satellite imagery without requiring custom training data.

Automate data labeling pipelines by using the automatic mask generation feature to produce zero-shot object masks across entire image datasets, or by interactively prompting the model with foreground points and bounding boxes for precise annotation.

Segment Anything Model works with medical and satellite imagery by utilizing prompt-based, zero-shot segmentation to cross domains, generating precise object masks from these specialized image formats without needing task-specific retraining.

You can export the SAM model to ONNX for production deployment, utilizing processor-based pipelines and supported ViT-H, ViT-L, and ViT-B variants to integrate zero-shot image segmentation into downstream applications.

ViT-H, ViT-L, and ViT-B variants offer different model sizes for image segmentation, allowing users to balance computational resources against accuracy when generating zero-shot object masks for interactive annotation or cross-domain processing.

You need PyTorch version 1.7.0 or higher and Transformers version 4.30.0 or higher as dependencies to run the zero-shot image segmentation model and process prompts for mask generation.