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

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

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

Source: https://github.com/kyegomez/VisionMamba