meta / sam-2

SAM 2: Segment Anything v2 (for Images)

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Run time and cost

This model costs approximately $0.062 to run on Replicate, or 16 runs per $1, but this varies depending on your inputs. It is also open source and you can run it on your own computer with Docker.

This model runs on Nvidia A40 (Large) GPU hardware. Predictions typically complete within 85 seconds. The predict time for this model varies significantly based on the inputs.

Readme

✨SAM 2: Segment Anything v2 for Images 🎥

NOTE: Note: Currently, we only support image inputs (not video yet) and exclusively offer the large variant of the model.

About

Implementation of SAM 2, a model for segmenting objects in images and videos using various prompts.

Limitations

  • Performance may vary depending on image/video quality and complexity.
  • Very fast or complex motions in videos might be challenging.
  • Higher resolutions provide more detail but require more processing time.

SAM 2 is a 🔥 model developed by Meta AI Research. It excels at segmenting objects in both images and videos with various types of prompts.

Core Model

model architecture
An overview of the SAM 2 framework.

SAM 2 uses a transformer architecture with streaming memory for real-time video processing. It builds on the original SAM model, extending its capabilities to video.

For more technical details, check out the Research paper.

Safety

⚠️ Users should be aware of potential ethical implications: - Ensure you have the right to use input images and videos, especially those featuring identifiable individuals. - Be responsible about generated content to avoid potential misuse. - Be cautious about using copyrighted material as inputs without permission.

Support

All credit goes to the Meta AI Research team

Citation

@article{ravi2024sam2,
  title={SAM 2: Segment Anything in Images and Videos},
  author={Ravi, Nikhila and Gabeur, Valentin and Hu, Yuan-Ting and Hu, Ronghang and Ryali, Chaitanya and Ma, Tengyu and Khedr, Haitham and R{\"a}dle, Roman and Rolland, Chloe and Gustafson, Laura and Mintun, Eric and Pan, Junting and Alwala, Kalyan Vasudev and Carion, Nicolas and Wu, Chao-Yuan and Girshick, Ross and Doll{\'a}r, Piotr and Feichtenhofer, Christoph},
  journal={arXiv preprint},
  year={2024}
}