segment-anything-model

Zero-shot image segmentation isolates objects in images without requiring fine-tuning. You can use point, box, or automatic prompts to generate object masks directly from image tensors using Meta AI's SAM architecture.

Automatic mask generation detects and segments all objects within an image without manual prompts. This feature allows you to quickly process entire images for automated data labeling or interactive annotation tools.

Yes, you need PyTorch, transformers, and the segment-anything library installed to execute inference. These dependencies provide the necessary environment to process image tensors and generate segmentation masks.

Generating training data with SAM involves using automatic mask generation or interactive prompts to quickly produce high-quality segmentation masks. These isolated object masks can then be exported for training other computer vision models.

Yes, zero-shot segmentation works with bounding box prompts on input images. You can pass a bounding box prompt alongside your image file to accurately isolate specific objects for mask generation.

SAM performs zero-shot segmentation without task-specific training, meaning it may lack highly specialized domain accuracy compared to fine-tuned models. It is best suited for rapid object isolation and generating preliminary training data.