SAM 2 (Segment Anything in Images and Videos), is a collection of foundation models from FAIR that aim to solve promptable visual segmentation in images and videos. See the
SAM 2 paper
for more information.
This is the Core ML version of
SAM 2.1 Tiny
, and is suitable for use with the
SAM2 Studio demo app
. It was converted in
float16
precision using
this fork
of the original code repository.
To cite the paper, model, or software, please use the below:
@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 arXiv:2408.00714},
url={https://arxiv.org/abs/2408.00714},
year={2024}
}
Runs of apple coreml-sam2.1-tiny on huggingface.co
140
Total runs
3
24-hour runs
-17
3-day runs
-3
7-day runs
-32
30-day runs
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