https://github.com/jaiwei98/MobileNetV4-pytorch

An unofficial implementation of MobileNetV4 in Pytorch
https://github.com/jaiwei98/MobileNetV4-pytorch

mnv4 mobilenet mobilenetv4 universalinvertedbottlenet

Last synced: 2 months ago
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An unofficial implementation of MobileNetV4 in Pytorch

• Host: GitHub
• URL: https://github.com/jaiwei98/MobileNetV4-pytorch
• Owner: jaiwei98
• License: mit
• Created: 2024-04-22T02:31:52.000Z (8 months ago)
• Default Branch: main
• Last Pushed: 2024-05-11T05:50:16.000Z (8 months ago)
• Last Synced: 2024-08-01T03:33:17.956Z (5 months ago)
• Topics: mnv4, mobilenet, mobilenetv4, universalinvertedbottlenet
• Language: Python
• Homepage: https://arxiv.org/abs/2404.10518
• Size: 12.7 KB
• Stars: 111
• Watchers: 3
• Forks: 9
• Open Issues: 6
• Metadata Files:
  • Readme: README.md

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README

        
# MobileNetV4-pytorch

An unofficial implementation of MobileNetV4 (MNv4) in Pytorch. 


There are 5 types of MNv4 as indicated in the MobileNetV4 -- Universal Models for the Mobile Ecosystem, e.g. 

- MobileNetV4ConvSmall (MNv4-Conv-S)

- MobileNetV4ConvMedium (MNv4-Conv-M)

- MobileNetV4ConvLarge (MNv4-Conv-L)

- MobileNetV4HybridMedium (MNv4-Hybrid-M)

- MobileNetV4HybridLarge (MNv4-Hybrid-L)

## Table of Content

- [How to init model](#mobilenetv4)

- [TODO](#todo)

- [Notes](#notes)

## MobileNetV4

This section mainly showed how to import MobileNetV4

```python

import torch

from mobilenet.mobilenetv4 import MobileNetV4

# Support ['MobileNetV4ConvSmall', 'MobileNetV4ConvMedium', 'MobileNetV4ConvLarge']

# Also supported ['MobileNetV4HybridMedium', 'MobileNetV4HybridLarge']

model = MobileNetV4("MobileNetV4ConvSmall")

# Check the trainable params

total_params = sum(p.numel() for p in model.parameters())

print(f"Number of parameters: {total_params}")

# Check the model's output shape

print("Check output shape ...")

x = torch.rand(1, 3, 224, 224)

y = model(x)

for i in y:

    print(i.shape)

```

## TODO

- [x] Support 'MobileNetV4HybridMedium' and 'MobileNetV4HybridLarge'

- [ ] Release pretrained weight (welcome any contributors to submit PR 😉)

## Notes

Note that there are few parts which not excatly the same as implementation in tensorflow 

-  The "fused_ib" block  

- The global average pooling layers at the end of model

## Credits

Some function and code are adapted and referenced from official repo tensorflow/models.

## Citations

```bibtex

@misc{qin2024mobilenetv4,

      title={MobileNetV4 -- Universal Models for the Mobile Ecosystem}, 

      author={Danfeng Qin and Chas Leichner and Manolis Delakis and Marco Fornoni and Shixin Luo and Fan Yang and Weijun Wang and Colby Banbury and Chengxi Ye and Berkin Akin and Vaibhav Aggarwal and Tenghui Zhu and Daniele Moro and Andrew Howard},

      year={2024},

      eprint={2404.10518},

      archivePrefix={arXiv},

      primaryClass={cs.CV}

}

```