segment-anything-model

Zero-shot image segmentation partitions images into objects without task-specific retraining. The Segment Anything Model uses lightweight prompts to perform cross-domain segmentation across natural, medical, and satellite imagery without finetuning.

To segment an image into objects, load a SAM predictor and provide lightweight prompts such as points, bounding boxes, or previous masks. You can apply SAM using combinations of these prompt types for interactive annotation and dataset curation.

Yes, you can deploy SAM using ONNX, HuggingFace, or PyTorch. These deployment options support multiple model variants, including ViT-B, ViT-L, and ViT-H, alongside preprocessing and post-processing tooling for pipeline integration.

Yes, SAM supports automatic mask generation alongside interactive prompt-based segmentation. You can use it to curate datasets and generate object masks across diverse domains without requiring manual prompts or task-specific finetuning.

Using the Segment Anything Model is an effective approach for zero-shot segmentation without finetuning. It leverages prompt-guided object detection to integrate into downstream computer vision pipelines for interactive annotation and dataset curation.