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- README.md +36 -0
- SAM2_1LargeImageEncoderFLOAT16.mlpackage/Data/com.apple.CoreML/model.mlmodel +3 -0
- SAM2_1LargeImageEncoderFLOAT16.mlpackage/Data/com.apple.CoreML/weights/weight.bin +3 -0
- SAM2_1LargeImageEncoderFLOAT16.mlpackage/Manifest.json +18 -0
- SAM2_1LargeMaskDecoderFLOAT16.mlpackage/Data/com.apple.CoreML/model.mlmodel +3 -0
- SAM2_1LargeMaskDecoderFLOAT16.mlpackage/Data/com.apple.CoreML/weights/weight.bin +3 -0
- SAM2_1LargeMaskDecoderFLOAT16.mlpackage/Manifest.json +18 -0
- SAM2_1LargePromptEncoderFLOAT16.mlpackage/Data/com.apple.CoreML/model.mlmodel +3 -0
- SAM2_1LargePromptEncoderFLOAT16.mlpackage/Data/com.apple.CoreML/weights/weight.bin +3 -0
- SAM2_1LargePromptEncoderFLOAT16.mlpackage/Manifest.json +18 -0
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README.md
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---
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license: apache-2.0
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pipeline_tag: mask-generation
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library_name: coreml
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---
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# SAM 2.1 Large Core ML
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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](https://arxiv.org/abs/2408.00714) for more information.
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This is the Core ML version of [SAM 2.1 Large](https://huggingface.co/facebook/sam2.1-hiera-large), and is suitable for use with the [SAM2 Studio demo app](https://github.com/huggingface/sam2-studio). It was converted in `float16` precision using [this fork](https://github.com/huggingface/segment-anything-2/tree/coreml-conversion) of the original code repository.
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## Download
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Install `huggingface-cli`
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```bash
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brew install huggingface-cli
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```
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```bash
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huggingface-cli download --local-dir models apple/coreml-sam2.1-large
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```
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## Citation
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To cite the paper, model, or software, please use the below:
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```
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@article{ravi2024sam2,
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title={SAM 2: Segment Anything in Images and Videos},
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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},
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journal={arXiv preprint arXiv:2408.00714},
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url={https://arxiv.org/abs/2408.00714},
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year={2024}
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}
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```
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