segment-anything-model

Zero-shot image segmentation uses prompt-based inputs like points and bounding boxes to generate binary masks without task-specific training. You provide prompts on an input image, and the model predicts the best object mask from the returned candidates.

Zero-shot segmentation creates object masks on diverse image domains without fine-tuning. Prompting works by using foreground and background points or bounding boxes to condition the model, which then refines and predicts accurate binary masks.

Yes, SAM applies zero-shot segmentation to medical or satellite imagery. It can automatically produce many candidate masks for downstream selection, filtering, or dataset creation without requiring domain-specific fine-tuning.

Yes, segment-anything-model supports ONNX deployment compatibility. You can export the model to ONNX to achieve faster inference workflows in production environments while retaining prompt-conditioned mask prediction capabilities.

Yes, you need PyTorch (1.7.0 or higher) and Transformers (4.30.0 or higher) along with the segment-anything dependency. These libraries provide the underlying neural network and model loading capabilities required for inference.