segment-anything-model

Zero-shot image segmentation generates object masks on custom photos without task-specific training. You provide prompts like points or boxes to guide the segmentation, and the model returns masks for arbitrary objects without requiring fine-tuning.

Point and box prompts can iteratively refine image segmentation masks. The workflow accepts points, boxes, or previous masks as inputs to guide the segmentation process and produce accurate object boundaries for your computer vision tasks.

Zero-shot segmentation works across Windows, macOS, and Linux. The workflow requires segment-anything, transformers>=4.30.0, and torch>=1.7.0, ensuring cross-platform compatibility for computer vision annotation and data curation tasks.

Automating data annotation for computer vision datasets is best achieved through zero-shot segmentation. By generating masks for arbitrary objects using flexible prompts, you can accelerate data preparation and model training workflows without manual labeling.

You need PyTorch and Transformers to run segment anything workflows. Specifically, the environment requires torch>=1.7.0 and transformers>=4.30.0, along with the segment-anything package, to execute the zero-shot mask generation process.

You can export segmentation models to ONNX for cross-platform deployment. The workflow supports ViT variants and ONNX export, allowing you to deploy the zero-shot segmentation capabilities across different platforms and environments.