Bias Loss & SkipblockNet

[ICCV 2021]Demo for the bias loss and SkipblockNet architecture presented in the paper "Bias Loss for Mobile Neural Networks".

Requirements

for installing required packages run pip install -r requirements.txt

Usage (SkipblockNet)

Pretrained SkipblockNet-m is available from Google Drive. For the testing please download and place the model in the same directory as the validation script.

python validate.py --data path/to/the/dataset

Usage (Bias loss)

Training and testing codes are available for DenseNet121, ShuffleNet V2 0.5x and ResNet18. To test the pretrained models please download corresponding model from the Google Drive and run the testing script in the bias loss directory

python test.py --checkpoint 'path to the checkpoint' --model 'name of the model' --data_path 'path to the cifar-100 dataset'

To train the models run the training script in the bias loss directory as follows:

python train.py --model 'name of the model to be trained' --data_path 'path to the cifar-100 dataset'

Introduction

"Bias Loss for Mobile Neural Networks"

By Lusine Abrahamyan, Valentin Ziatchin, Yiming Chen and Nikos Deligiannis.

Approach (SkipblockNet)

Performance (SkipblockNet)

SkipNet beats other SOTA lightweight CNNs such as MobileNetV3 and FBNet.

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Approach (Bias loss)

The bias loss is a dynamically scaled cross-entropy loss, where the scale decays as the variance of data point decreases.

Performance (Bias loss)

Bellow is the results of the pretrained models that can be found in the Google Drive

Model	Top-1 bias loss	Top-1 CE
ResNet18	75.51%	74.33%
DenseNet121	77.83%	75.98%
ShuffleNet V2 0.5x	72.00%	71.55%

Citation

If you find the code useful for your research, please consider citing our works

@inproceedings{abrahamyan2021bias,
  title={Bias Loss for Mobile Neural Networks},
  author={Abrahamyan, Lusine and Ziatchin, Valentin and Chen, Yiming and Deligiannis, Nikos},
  booktitle={Proceedings of the IEEE/CVF International Conference on Computer Vision},
  pages={6556--6566},
  year={2021}
}

Acknowledgement

Codes is heavily modified from pytorch-vision and pytorch-cifar100.