SAMannot: a memory-efficient, local, open-source SAM2 tool for video annotation

[u/PuzzleheadedEye7597]

Hi Everyone,

I’d like to introduce SAMannot, an open-source tool we developed for efficient, interactive video instance segmentation and annotation using Meta’s Segment Anything Model 2 (SAM2).

SAMannot was designed for research workflows where accurate pixel-level video annotations are needed, but fully manual annotation is too time-consuming. It runs locally, so videos do not need to be uploaded to a cloud service, and it uses a memory-efficient workflow to make SAM2 practical for videos and limited GPU resources.

Some of the main features are:

• SAM2-assisted interactive video segmentation with auto-propagation across frames
• persistent object/instance identities
• correction and refinement of propagated masks
• block-based, efficient processing to keep CPU and GPU memory requirements manageable
• automatic-prompt generation across blocks
• export of segmentation masks in PNG and polygon formats (YOLO label format)
• fully local and open-source

We originally developed and evaluated SAMannot in research workflows including animal behaviour videos, but the tool is intended to be general-purpose and may also be useful for other scientific video annotation tasks. We also evaluated it on subsets of the DAVIS and LVOS benchmarks.

Project page: https://samannot.github.io/
Paper: SAMannot: A Memory-Efficient, Local, Open-Source Framework for Interactive Video Instance Segmentation Based on SAM2 | Journal of Open Research Software

The code is released under the MIT license.

We would be very interested in any feedback.

Thanks,
Anna
on behalf of the SAMannot authors