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# 🌊 SAMURAI: Adapting Segment Anything Model for Zero-Shot Visual Tracking with Motion-Aware Memory
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# 🌊 SAMURAI: Adapting Segment Anything Model for Zero-Shot Visual Tracking with Motion-Aware Memory
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[[Arxiv]]() [[Project Page]]() [[Raw Results]]()
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[[Arxiv]](https://arxiv.org/abs/2411.11922) [[Project Page]](https://yangchris11.github.io/samurai/)
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This repository is the official implementation of SAMURAI: Adapting Segment Anything Model for Zero-Shot Visual Tracking with Motion-Aware Memory
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This repository is the official implementation of SAMURAI: Adapting Segment Anything Model for Zero-Shot Visual Tracking with Motion-Aware Memory
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https://github.com/user-attachments/assets/9d368ca7-2e9b-4fed-9da0-d2efbf620d88
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https://github.com/user-attachments/assets/9d368ca7-2e9b-4fed-9da0-d2efbf620d88
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## Getting Started
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## Code
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#### SAMURAI Installation
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Coming soon!
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SAM 2 needs to be installed first before use. The code requires `python>=3.10`, as well as `torch>=2.3.1` and `torchvision>=0.18.1`. Please follow the instructions [here](https://github.com/facebookresearch/sam2?tab=readme-ov-file) to install both PyTorch and TorchVision dependencies. You can install **the SAMURAI version** of SAM 2 on a GPU machine using:
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```
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cd sam2
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pip install -e .
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pip install -e ".[notebooks]"
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```
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Please see [INSTALL.md](https://github.com/facebookresearch/sam2/blob/main/INSTALL.md) from the original SAM 2 repository for FAQs on potential issues and solutions.
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```
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pip install requirements.txt
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```
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#### SAM 2.1 Checkpoint Download
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```
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cd checkpoints && \
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./download_ckpts.sh && \
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cd ..
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```
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#### Dataset Preparation
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#### Run SAMURAI
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<!-- ## Main Results -->
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## Acknowledgment
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## Acknowledgment
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@@ -55,11 +28,13 @@ Please consider citing our paper and the wonderful `SAM 2` if you found our work
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year={2024}
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year={2024}
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}
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}
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@article{yang2024samurai,
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@misc{yang2024samurai,
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title={Adapting Segment Anything Model for Zero-Shot Visual Tracking with Motion-Aware Memory},
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title={SAMURAI: Adapting Segment Anything Model for Zero-Shot Visual Tracking with Motion-Aware Memory},
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author={Yang, Cheng-Yen and Huang, Hsiang-Wei and Chai, Wenhao and Jiang, Zhongyu and Hwang, Jenq-Neng},
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author={Cheng-Yen Yang and Hsiang-Wei Huang and Wenhao Chai and Zhongyu Jiang and Jenq-Neng Hwang},
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journal={},
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year={2024},
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url={},
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eprint={2411.11922},
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year={2024}
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archivePrefix={arXiv},
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primaryClass={cs.CV},
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url={https://arxiv.org/abs/2411.11922},
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}
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}
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```
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```
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