Vim-tiny-midclstok

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Vim Model Card

Model Details

Vision Mamba (Vim) is a generic backbone trained on the ImageNet-1K dataset for vision tasks.

• Developed by: HUST, Horizon Robotics, BAAI
• 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. See more details in this paper.

Evaluation

Vim-tiny is evaluated on ImageNet-1K val set, and achieves 76.1% Top-1 Acc. By further finetuning at finer granularity, Vim-tiny achieves 78.3% Top-1 Acc. See more details in this paper.

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}
}