The repository provides code for running inference with the Meta Segment Anything Model 2 (SAM 2), links for downloading the trained model checkpoints, and example notebooks that show how to use the model.
SAM 2 is a foundation model for promptable visual segmentation in images and videos, extending the original Segment Anything Model to handle video by treating images as single-frame videos. It features a transformer architecture with streaming memory for real-time video processing and is trained on the largest video segmentation dataset to date. This project is aimed at researchers and developers working on computer vision tasks like object tracking, video segmentation, and interactive segmentation applications.
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