Skip to content

Latest commit

 

History

History
27 lines (21 loc) · 3.75 KB

RELEASE_NOTES.md

File metadata and controls

27 lines (21 loc) · 3.75 KB
Raw

SAM 2 release notes

12/11/2024 -- full model compilation for a major VOS speedup and a new SAM2VideoPredictor to better handle multi-object tracking

  • We now support torch.compile of the entire SAM 2 model on videos, which can be turned on by setting vos_optimized=True in build_sam2_video_predictor (it uses the new SAM2VideoPredictorVOS predictor class in sam2/sam2_video_predictor.py).
    • Compared to the previous setting (which only compiles the image encoder backbone), the new full model compilation gives a major speedup in inference FPS.
    • In the VOS prediction script tools/vos_inference.py, you can specify this option in tools/vos_inference.py via the --use_vos_optimized_video_predictor flag.
    • Note that turning on this flag might introduce a small variance in the predictions due to numerical differences caused by torch.compile of the full model.
    • PyTorch 2.5.1 is the minimum version for full support of this feature. (Earlier PyTorch versions might run into compilation errors in some cases.) Therefore, we have updated the minimum PyTorch version to 2.5.1 accordingly in the installation scripts.
  • We also update the implementation of the SAM2VideoPredictor class for the SAM 2 video prediction in sam2/sam2_video_predictor.py, which allows for independent per-object inference. Specifically, in the new SAM2VideoPredictor:
    • Now we handle the inference of each object independently (as if we are opening a separate session for each object) while sharing their backbone features.
    • This change allows us to relax the assumption of prompting for multi-object tracking. Previously (due to the batching behavior in inference), if a video frame receives clicks for only a subset of objects, the rest of the (non-prompted) objects are assumed to be non-existent in this frame (i.e., in such frames, the user is telling SAM 2 that the rest of the objects don't appear). Now, if a frame receives clicks for only a subset of objects, we do not make any assumptions about the remaining (non-prompted) objects (i.e., now each object is handled independently and is not affected by how other objects are prompted). As a result, we allow adding new objects after tracking starts after this change (which was previously a restriction on usage).
    • We believe that the new version is a more natural inference behavior and therefore switched to it as the default behavior. The previous implementation of SAM2VideoPredictor is backed up to in sam2/sam2_video_predictor_legacy.py. All the VOS inference results using tools/vos_inference.py should remain the same after this change to the SAM2VideoPredictor class.

09/30/2024 -- SAM 2.1 Developer Suite (new checkpoints, training code, web demo) is released

  • A new suite of improved model checkpoints (denoted as SAM 2.1) are released. See Model Description for details.
    • To use the new SAM 2.1 checkpoints, you need the latest model code from this repo. If you have installed an earlier version of this repo, please first uninstall the previous version via pip uninstall SAM-2, pull the latest code from this repo (with git pull), and then reinstall the repo following Installation below.
  • The training (and fine-tuning) code has been released. See training/README.md on how to get started.
  • The frontend + backend code for the SAM 2 web demo has been released. See demo/README.md for details.

07/29/2024 -- SAM 2 is released