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  license: apache-2.0
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  license: apache-2.0
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+ <br>
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+ # Vim Model Card
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+ ## Model Details
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+ Vision Mamba (Vim) is a generic backbone trained on the ImageNet-1K dataset for vision tasks.
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+ - **Developed by:** [HUST](https://english.hust.edu.cn/), [Horizon Robotics](https://en.horizon.cc/), [BAAI](https://www.baai.ac.cn/english.html)
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+ - **Model type:** A generic vision backbone based on the bidirectional state space model (SSM) architecture.
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+ - **License:** Non-commercial license
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+ ### Model Sources
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+ - **Repository:** https://github.com/hustvl/Vim
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+ - **Paper:** https://arxiv.org/abs/2401.09417
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+ ## Uses
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+ The primary use of Vim is research on vision tasks, e.g., classification, segmentation, detection, and instance segmentation, with an SSM-based backbone.
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+ The primary intended users of the model are researchers and hobbyists in computer vision, machine learning, and artificial intelligence.
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+ ## How to Get Started with the Model
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+ - You can replace the backbone for vision tasks with the proposed Vim: https://github.com/hustvl/Vim/blob/main/vim/models_mamba.py
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+ - Then you can load this checkpoint and start training.
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+
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+ ## Training Details
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+ Vim is pretrained on ImageNet-1K with classification supervision.
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+ The training data is around 1.3M images from [ImageNet-1K dataset](https://www.image-net.org/challenges/LSVRC/2012/).
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+ See more details in this [paper](https://arxiv.org/abs/2401.09417).
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+ ## Evaluation
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+ Vim is evaluated on ImageNet-1K val set, and achieves 73.1% Top-1 Acc. See more details in this [paper](https://arxiv.org/abs/2401.09417).
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+ ## Additional Information
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+
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+ ### Citation Information
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+ ```
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+ @article{vim,
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+ title={Vision Mamba: Efficient Visual Representation Learning with Bidirectional State Space Model},
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+ author={Lianghui Zhu and Bencheng Liao and Qian Zhang and Xinlong Wang and Wenyu Liu and Xinggang Wang},
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+ journal={arXiv preprint arXiv:2401.09417},
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+ year={2024}
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+ }
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+ ```
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