segment-anything-model

You can perform zero-shot image segmentation by providing an image alongside a point, bounding box, or mask prompt. The model then outputs accurate segmentation masks without requiring any fine-tuning.

The segment anything model accepts points, bounding boxes, and previous masks as prompts. These flexible inputs allow you to define specific segmentation areas within an image to generate precise masks.

Yes, using PyTorch and Transformers, you can automatically generate all object masks from an image. The model produces high-quality masks trained on a large dataset of 1.1 billion masks for accuracy.

For computer vision tasks, you can choose from fast (ViT-B), medium (ViT-L), and accurate (ViT-H) model sizes. This allows you to balance inference speed and segmentation accuracy based on your project requirements.

You can export the model using ONNX format to deploy it in browsers and edge devices. This enables real-time segmentation capabilities directly on client-side hardware without server-side processing.