MobileNetV4-PyTorch

Overview

This repository provides a PyTorch replication of the MobileNetV4 architecture as described in the paper "MobileNetV4: Universal Models for the Mobile Ecosystem". The implementation aims to mimic the architecture closely for all five variants: - MobileNetV4ConvSmall - MobileNetV4ConvMedium - MobileNetV4ConvLarge - MobileNetV4HybridMedium - MobileNetV4HybridLarge

Repository Structure

• env/: Contains the environment YAML file to set up the necessary dependencies.
• logs/: Contains the architecture details of the different MobileNetV4 variants.
• paper/: Contains the original MobileNetV4 paper for reference.
• MobileNetV4.py: Contains the feature extractor for MobileNetV4 architectures.
• nn_blocks.py: Contains neural network block definitions used in the MobileNetV4 architecture.
• test.py: Contains the classifier and script for testing the implementations.

Installation

To create the environment with the necessary dependencies, use the provided YAML file:

bash conda env create -f env/MobileNetV4_env.yml conda activate MobileNetV4-PyTorch

Usage

Training

To train a MobileNetV4 model on your dataset, modify the test.py script with your dataset and training configurations.

Pre-trained Weights

For pre-trained weights on ImageNet, you can use the weights provided by timm.

Example

```python import torch from test import MobileNetV4WithClassifier

Example usage

model = MobileNetV4WithClassifier(modelname='MobileNetV4ConvSmall', numclasses=1000) inputtensor = torch.randn(1, 3, 224, 224) output = model(inputtensor) print(output) ```

Citations

If you find this work useful, please cite the original MobileNetV4 paper: bibtex @article{MobileNetV4, title={MobileNetV4: Universal Models for the Mobile Ecosystem}, author={Author Names}, journal={arXiv preprint arXiv:2404.10518v1}, year={2024} } If you use this work, please cite it as follows: bibtex @misc{MobileNetV4-PyTorch, author = {Muhammad Junaid Ali Asif Raja}, title = {MobileNetV4-PyTorch}, year = {2024}, url = {https://github.com/junaidaliop/MobileNetV4}, note = {Version 1.0.0} }

Contact

For research collaborations or any queries, please email me at muhammadjunaidaliasifraja@gmail.com

Contributions

Contributions are welcome! Please submit a pull request or open an issue to discuss your ideas.

TODO

• [ ] Train the model on ImageNet to attain weights
• [ ] Train the model on CIFAR-100
• [ ] Train the model on CIFAR-10

Star the Repository

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Acknowledgment

This is an unofficial implementation of MobileNetV4 in PyTorch. To the best of my ability, I believe this is the closest implementation to the original work found at TensorFlow MobileNetV4 Implementation.

For the official TensorFlow implementation, please visit: TensorFlow MobileNetV4 Implementation.