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.
pip install vision-mambaimport 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
@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}
}MIT
- Create training script for imagenet
- Create a visual mamba for facial recognition