[](https://discord.gg/qUtxnK2NMf)
# Vision Mamba
Implementation of Vision Mamba from the paper: "Vision Mamba: Efficient Visual Representation Learning with Bidirectional State Space Model" It's 2.8x faster than DeiT and saves 86.8% GPU memory when performing batch inference to extract features on high-res images.
[PAPER LINK](https://arxiv.org/abs/2401.09417)
## Installation
```bash
pip install vision-mamba
```
# Usage
```python
import torch
from vision_mamba import Vim
# Forward pass
x = torch.randn(1, 3, 224, 224) # Input tensor with shape (batch_size, channels, height, width)
# Model
model = Vim(
dim=256, # Dimension of the transformer model
heads=8, # Number of attention heads
dt_rank=32, # Rank of the dynamic routing matrix
dim_inner=256, # Inner dimension of the transformer model
d_state=256, # Dimension of the state vector
num_classes=1000, # Number of output classes
image_size=224, # Size of the input image
patch_size=16, # Size of each image patch
channels=3, # Number of input channels
dropout=0.1, # Dropout rate
depth=12, # Depth of the transformer model
)
# Forward pass
out = model(x) # Output tensor from the model
print(out.shape) # Print the shape of the output tensor
print(out) # Print the output tensor
```
## Citation
```bibtex
@misc{zhu2024vision,
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},
year={2024},
eprint={2401.09417},
archivePrefix={arXiv},
primaryClass={cs.CV}
}
```
# License
MIT
# Todo
- [ ] Create training script for imagenet
- [ ] Create a visual mamba for facial recognition
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"description": "[](https://discord.gg/qUtxnK2NMf)\n\n# Vision Mamba\nImplementation of Vision Mamba from the paper: \"Vision Mamba: Efficient Visual Representation Learning with Bidirectional State Space Model\" It's 2.8x faster than DeiT and saves 86.8% GPU memory when performing batch inference to extract features on high-res images. \n\n[PAPER LINK](https://arxiv.org/abs/2401.09417)\n\n## Installation\n\n```bash\npip install vision-mamba\n```\n\n# Usage\n```python\nimport torch\nfrom vision_mamba import Vim\n\n# Forward pass\nx = torch.randn(1, 3, 224, 224) # Input tensor with shape (batch_size, channels, height, width)\n\n# Model\nmodel = Vim(\n dim=256, # Dimension of the transformer model\n heads=8, # Number of attention heads\n dt_rank=32, # Rank of the dynamic routing matrix\n dim_inner=256, # Inner dimension of the transformer model\n d_state=256, # Dimension of the state vector\n num_classes=1000, # Number of output classes\n image_size=224, # Size of the input image\n patch_size=16, # Size of each image patch\n channels=3, # Number of input channels\n dropout=0.1, # Dropout rate\n depth=12, # Depth of the transformer model\n)\n\n# Forward pass\nout = model(x) # Output tensor from the model\nprint(out.shape) # Print the shape of the output tensor\nprint(out) # Print the output tensor\n\n\n\n```\n\n\n\n## Citation\n```bibtex\n@misc{zhu2024vision,\n title={Vision Mamba: Efficient Visual Representation Learning with Bidirectional State Space Model}, \n author={Lianghui Zhu and Bencheng Liao and Qian Zhang and Xinlong Wang and Wenyu Liu and Xinggang Wang},\n year={2024},\n eprint={2401.09417},\n archivePrefix={arXiv},\n primaryClass={cs.CV}\n}\n```\n\n# License\nMIT\n\n\n\n# Todo\n- [ ] Create training script for imagenet\n- [ ] Create a visual mamba for facial recognition",
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