Paper Link:Deep Rotation Equivirant Network
Caffe version code:https://github.com/microljy/DREN
- Install TensorFlow. Note that TensorFlow 0.12.0 is not supported.
- Install matlab for preprocessing Rotated-MNIST.
First, download the data and do preprocessing.
cd DREN_ROOT/data/rmnist
sh get_data.sh
matlab < data_preprocess.m
python generate_npy.py
rm data.mat
You can train the model with this command.
python train_rmnist.py --model [MODEL_NAME]
The params MODEL_NAME could be z2cnn,dren_z2cnn or dren_z2cnn_x4.
| model | error |
|---|---|
| Z2CNN | 4.58% |
| DREN_Z2CNN | 3.08% |
| DREN_Z2CNN_x4 | 1.76% |
First, download the data and do preprocessing.
cd data/cifar-10
wget http://www.cs.toronto.edu/~kriz/cifar-10-python.tar.gz
tar -zxvf cifar-10-python.tar.gz
rm cifar-10-python.tar.gz
You can train the model with this command.
python train_rmnist.py --model [MODEL_NAME]
The params MODEL_NAME could be resnet20,dren_resnet20_2b or dren_resnet20_2b_x4.
| model | error |
|---|---|
| Resnet-20 | 9.00% |
| DREN_Resnet-20 | 8.51% |
| DREN_Resnet-20_x4 | 7.17% |
DREN can be used to boost the performance of classification of images that have rotation symmetry, such as aerial image, microscope images, CT images and so on. We have tested the DREN in lung nodule detection and found it helpful.
Please cite DREN in your publications if it helps your research:
@article{li2018deep,
title={Deep rotation equivariant network},
author={Li, Junying and Yang, Zichen and Liu, Haifeng and Cai, Deng},
journal={Neurocomputing},
volume={290},
pages={26--33},
year={2018},
publisher={Elsevier}
}