segment-anything-model

Automatic mask generation works by applying zero-shot image segmentation to any image domain without fine-tuning, using advanced deep learning techniques trained on 1.1 billion masks.

Flexible prompts for image segmentation include points, bounding boxes, or previous masks, allowing interactive annotation and object detection pipelines across various image processing scenarios.

ONNX export deploys deep learning models in browsers and edge devices using onnxruntime, converting trained image segmentation models for lightweight inference environments.

Zero-shot image segmentation applies to medical and satellite imaging scenarios, processing these specialized image domains without requiring additional fine-tuning or domain-specific training.

Zero-shot image segmentation requires deep learning frameworks including torch and transformers, alongside specific libraries like opencv-python and pycocotools for image processing.

Zero-shot image segmentation relies on predefined model sizes for speed and accuracy trade-offs, requiring substantial computational resources for the most accurate model trained on 11 million images.