segment-anything-model

Zero-shot image segmentation can be performed using a pre-trained Vision Transformer model to segment any object in images without task-specific training. You can use point, bounding box, or previous mask prompts to isolate target objects.

Automatic object mask generation is supported natively by the model, allowing you to produce all object masks for an image without providing manual prompts. This is useful for interactive annotation and generating training data for other vision models.

Yes, zero-shot segmentation with this approach requires PyTorch and Transformers dependencies. It uses a pre-trained Vision Transformer architecture and integrates with the segment-anything library to process image prompts.

Point and bounding box prompts are fully supported for interactive image segmentation. The model accepts these flexible prompts alongside previous masks to accurately isolate specific objects within your target image.

Three Vision Transformer model sizes are available: ViT-B for the fastest processing, ViT-L for a balanced approach, and ViT-H for the highest accuracy in object detection and segmentation tasks.

You can export the image segmentation models using ONNX format to deploy them in browsers and edge devices. This allows zero-shot segmentation capabilities to run efficiently outside of standard PyTorch environments.