Update README.md

README.md

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-license: apache-2.0
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+---
+license: apache-2.0
+---
+
+
+<br>
+
+# Vim Model Card
+
+## Model Details
+
+Vision Mamba (Vim) is a generic backbone trained on the ImageNet-1K dataset for vision tasks.
+
+- **Developed by:** [HUST](https://english.hust.edu.cn/), [Horizon Robotics](https://en.horizon.cc/), [BAAI](https://www.baai.ac.cn/english.html)
+- **Model type:** A generic vision backbone based on the bidirectional state space model (SSM) architecture.
+- **License:** Non-commercial license
+
+
+### Model Sources
+
+- **Repository:** https://github.com/hustvl/Vim
+- **Paper:** https://arxiv.org/abs/2401.09417
+
+## Uses
+
+The primary use of Vim is research on vision tasks, e.g., classification, segmentation, detection, and instance segmentation, with an SSM-based backbone.
+The primary intended users of the model are researchers and hobbyists in computer vision, machine learning, and artificial intelligence.
+
+## How to Get Started with the Model
+
+- You can replace the backbone for vision tasks with the proposed Vim: https://github.com/hustvl/Vim/blob/main/vim/models_mamba.py
+- Then you can load this checkpoint and start training.
+
+## Training Details
+
+Vim is pretrained on ImageNet-1K with classification supervision.
+The training data is around 1.3M images from [ImageNet-1K dataset](https://www.image-net.org/challenges/LSVRC/2012/).
+See more details in this [paper](https://arxiv.org/abs/2401.09417).
+
+## Evaluation
+
+Vim-base is evaluated on ImageNet-1K val set, and achieves 81.9% Top-1 Acc. See more details in this [paper](https://arxiv.org/abs/2401.09417).
+
+## Additional Information
+
+### Citation Information
+
+```
+ @article{vim,
+  title={Vision Mamba: Efficient Visual Representation Learning with Bidirectional State Space Model},
+  author={Lianghui Zhu and Bencheng Liao and Qian Zhang and Xinlong Wang and Wenyu Liu and Xinggang Wang},
+  journal={arXiv preprint arXiv:2401.09417},
+  year={2024}
+}
+```
+