segment-anything-model

Zero-shot image segmentation is achieved using Meta AI's Segment Anything Model (SAM). It allows you to segment any object in images through interactive prompts like points and bounding boxes, or via automatic mask generation, without requiring task-specific training.

Yes, you can use points and bounding boxes as interactive prompts to segment specific regions in an image. The Segment Anything Model (SAM) facilitates precise object identification and masking based on these coordinate inputs.

Yes, the Segment Anything Model (SAM) supports ONNX export for deployment. This allows you to transition the zero-shot image segmentation capabilities from PyTorch into the ONNX runtime environment for optimized inference.

The best way to automatically identify and mask all objects within an image is using the automatic mask generation feature of the Segment Anything Model (SAM). It provides zero-shot segmentation to detect and mask every object present.

The available model variants for zero-shot object detection and masking are ViT-B, ViT-L, and ViT-H. These Segment Anything Model (SAM) architectures provide different scales of processing power for image segmentation tasks.