segment-anything-model

Zero-shot image segmentation generates accurate object masks using points, boxes, or masks as prompts. Meta's Segment Anything Model enables this across diverse domains without requiring task-specific training.

Yes, interactive image annotation supports point, box, and mask prompts to generate segmentation boundaries. This enables rapid object extraction and labeling workflows for AI training data across various domains.

Yes, running SAM for image segmentation requires the segment-anything package with transformers>=4.30.0 and torch>=1.7.0 installed. These dependencies provide the deep-learning environment needed for mask generation.

Zero-shot segmentation applies across medical imaging and satellite imagery domains. SAM processes these multimodal tasks using point, box, or mask prompts to obtain accurate object masks without domain-specific retraining.

Using zero-shot prompts with SAM is an efficient way to extract objects from images for computer vision datasets. It generates accurate segmentation masks for interactive annotation, reducing manual data labeling effort.