segment-anything-model

Generate object masks by applying the Segment Anything Model (SAM) to images using point or box prompts. This zero-shot image segmentation automates mask generation without requiring model fine-tuning.

Zero-shot image segmentation generates masks for objects with minimal prompts, accelerating dataset labeling. You need it to automate data preparation workflows without training custom segmentation models.

Yes, you need Python, PyTorch, transformers, and the segment-anything library to run SAM. You also need SAM checkpoints (vit-b, vit-l, or vit-h) to initialize the model for image annotation.

Yes, SAM supports medical and satellite imagery analysis on Linux, macOS, and Windows. It generates rapid masks from points or boxes for interactive object extraction in these specialized images.

Balance speed and accuracy for mask generation by selecting among SAM's flexible model variants: ViT-B, ViT-L, or ViT-H. Smaller checkpoints process faster while larger ones yield higher accuracy.

Yes, SAM supports ONNX deployment for scalable batch processing pipelines. This interoperability allows automated mask generation to integrate efficiently within larger Python workflows.