Vision Mamba - Pytorch
Project description
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.
Installation
pip install vision-mamba
Usage
import torch
from vision_mamba.model import Vim
# Create a random tensor
x = torch.randn(1, 3, 224, 224)
# Create an instance of the Vim model
model = Vim(
dim=256, # Dimension of the model
heads=8, # Number of attention heads
dt_rank=32, # Rank of the dynamic routing tensor
dim_inner=256, # Inner dimension of the model
d_state=256, # State dimension of the model
num_classes=1000, # Number of output classes
image_size=224, # Size of the input image
patch_size=16, # Size of the image patch
channels=3, # Number of input channels
dropout=0.1, # Dropout rate
depth=12, # Depth of the model
)
# Perform a forward pass through the model
out = model(x)
# Print the shape and output of the forward pass
print(out.shape)
print(out)
Citation
@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
- Fix the encoder block with the forward and backward convolutions
- Make a training script for imagenet
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