In this repository, we introduce a cutting-edge technique termed "Frequency Regularization" that remarkably compresses a UNet model from 31 million parameters down to a mere 759 non-zero parameters without compromising on performance. This technique has been meticulously tailored and evaluated, achieving an impressive Dice Score of over 97% on the Carvana Image Masking Challenge dataset. The original UNet model has a size of 366MB, but with our frequency regularization applied, the size is drastically reduced to 4KB, as demonstrated in the unet_fr.tar.xz file included in this demo.
- Ensure that you have the
imageiopackage installed for loading testing images. Follow the installation instructions if you don't have it installed yet.
- Clone this repository to your local machine.
- Navigate to the project directory.
- Execute the following command to run the demo:
bash run.sh
Due to the double-blind policy, comments within the code have been omitted. However, the demonstration is structured to be straightforward and the execution command provided should seamlessly run the demo.
Feel free to reach out for any inquiries or further clarifications regarding the implementation and performance of the frequency regularization technique on the UNet model.
| Milestones | Status |
|---|---|
| Initial U-net Model | ✔️ Completed |
| Additional Model Releases | 🔜 Upcoming |
| Example Training Code | 🔜 Upcoming |
| Pip Repository Creation | ✔️ Completed |
The initial model utilizing the U-net architecture has been developed and rigorously validated. This model serves as a compelling testament to the power of Frequency Regularization to markedly diminish model size without sacrificing performance integrity.
Future efforts will focus on disseminating a variety of models with accompanying training code to elucidate the breadth and potential of Frequency Regularization. These models will span multiple architectures and datasets, affirming the method's generalizability and strength.
We have accomplished the development of a pip repository for our Frequency Regularization technique. You can now integrate Frequency Regularization into your projects by simply running pip install frereg. This step is instrumental in simplifying the deployment of condensed yet potent models in pragmatic applications.
With the newly created pip repository, installation and execution have become more streamlined. Please ensure you have completed the following steps to use the Frequency Regularization technique with your UNet models:
You can install the Frequency Regularization package by executing the following command in your environment:
pip install freregThis command will install the necessary components to apply Frequency Regularization to your convolutional neural network models.
If you find our Frequency Regularization technique intriguing or utilize it in your research, we kindly encourage you to cite our paper:
@ARTICLE{10266314,
author={Zhao, Chenqiu and Dong, Guanfang and Zhang, Shupei and Tan, Zijie and Basu, Anup},
journal={IEEE Access},
title={Frequency Regularization: Reducing Information Redundancy in Convolutional Neural Networks},
year={2023},
volume={11},
number={},
pages={106793-106802},
doi={10.1109/ACCESS.2023.3320642}
}The paper is published in IEEE Access, and you can access it here.
Title: Frequency Regularization: Reducing Information Redundancy in Convolutional Neural Networks
Your acknowledgment greatly supports our ongoing research and contributes to fostering advancements in network compression techniques.