segment-anything-model

Zero-shot image segmentation is achieved by using the Segment Anything Model (SAM) to automatically segment objects in images without fine-tuning. It supports point, bounding box, or mask prompts to isolate regions.

Zero-shot image segmentation uses point, bounding box, or previous mask prompts to identify target objects within an image. The Segment Anything Model (SAM) processes these prompts to generate accurate object masks without requiring task-specific training.

Yes, the Segment Anything Model (SAM) supports automatic segmentation to generate all object masks within an image automatically. It applies to interactive annotation, data generation, and object detection pipelines without requiring manual prompts.

Yes, running the Segment Anything Model requires installing the segment-anything library alongside compatible Python libraries, specifically transformers>=4.30.0 and torch>=1.7.0, to execute the underlying deep learning operations.

Yes, the Segment Anything Model supports ONNX export, allowing you to deploy the zero-shot segmentation models in browsers and on edge devices. This enables flexible inference outside of standard Python environments.

Zero-shot segmentation offers three model sizes: ViT-B for the fastest processing, ViT-L for a balanced approach, and ViT-H for the highest accuracy. Choose based on your specific computational resources and precision requirements.