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Oriented Response Networks

[Home] [Project] [Paper] [Supp] [Poster]

illustration

🎉Update: Reimplemented ORN that supports modern PyTorch.

  • Tested with PyTorch 1.12.0 (Ubuntu / GTX 2080 Ti)
  • A New helper function upgrade_to_orn for easy model conversion.
  • Predefined ORN-upgraded models (OR-VGG, OR-ResNet, OR-Inception, OR-WRN, etc.).

Please check the pytorch-v2 branch for more details.

Torch Implementation

The torch branch contains:

  • the official torch implementation of ORN.
  • the MNIST-Variants demo.

Please follow the instruction below to install it and run the experiment demo.

Prerequisites

  • Linux (tested on ubuntu 14.04LTS)
  • NVIDIA GPU + CUDA CuDNN (CPU mode and CUDA without CuDNN mode are also available but significantly slower)
  • Torch7

Getting started

You can setup everything via a single command wget -O - https://git.io/vHCMI | bash or do it manually in case something goes wrong:

  1. install the dependencies (required by the demo code):

  2. clone the torch branch:

    # git version must be greater than 1.9.10
git clone https://github.com/ZhouYanzhao/ORN.git -b torch --single-branch ORN.torch
cd ORN.torch
export DIR=$(pwd)

  3. install ORN:

    cd $DIR/install
# install the CPU/GPU/CuDNN version ORN.
bash install.sh

  4. unzip the MNIST dataset:

    cd $DIR/demo/datasets
unzip MNIST

  5. run the MNIST-Variants demo:

    cd $DIR/demo
# you can modify the script to test different hyper-parameters
bash ./scripts/Train_MNIST.sh

Trouble shooting

If you run into 'cudnn.find' not found, update Torch7 to the latest version via cd <TORCH_DIR> && bash ./update.sh then re-install everything.

More experiments

CIFAR 10/100

You can train the OR-WideResNet model (converted from WideResNet by simply replacing Conv layers with ORConv layers) on CIFAR dataset with WRN.

dataset=cifar10_original.t7 model=or-wrn widen_factor=4 depth=40 ./scripts/train_cifar.sh

With exactly the same settings, ORN-augmented WideResNet achieves state-of-the-art result while using significantly fewer parameters.

CIFAR

Network Params CIFAR-10 (ZCA) CIFAR-10 (mean/std) CIFAR-100 (ZCA) CIFAR-100 (mean/std)
DenseNet-100-12-dropout 7.0M - 4.10 - 20.20
DenseNet-190-40-dropout 25.6M - 3.46 - 17.18
WRN-40-4 8.9M 4.97 4.53 22.89 21.18
WRN-28-10-dropout 36.5M 4.17 3.89 20.50 18.85
WRN-40-10-dropout 55.8M - 3.80 - 18.3
ORN-40-4(1/2) 4.5M 4.13 3.43 21.24 18.82
ORN-28-10(1/2)-dropout 18.2M 3.52 2.98 19.22 16.15

Table.1 Test error (%) on CIFAR10/100 dataset with flip/translation augmentation)

ImageNet

ILSVRC2012

The effectiveness of ORN is further verified on large scale data. The OR-ResNet-18 model upgraded from ResNet-18 yields significant better performance when using similar parameters.

Network Params Top1-Error Top5-Error
ResNet-18 11.7M 30.614 10.98
OR-ResNet-18 11.4M 28.916 9.88

Table.2 Validation error (%) on ILSVRC-2012 dataset.

You can use facebook.resnet.torch to train the OR-ResNet-18 model from scratch or finetune it on your data by using the pre-trained weights.

-- To fill the model with the pre-trained weights:
model = require('or-resnet.lua')({tensorType='torch.CudaTensor', pretrained='or-resnet18_weights.t7'})

A more specific demo notebook of using the pre-trained OR-ResNet to classify images can be found here.

PyTorch Implementation (Deprecated)

Please check the pytorch-v2 branch for more details.

The pytorch branch contains:

  • the official pytorch implementation of ORN (alpha version supports 1x1/3x3 ARFs with 4/8 orientation channels only).
  • the MNIST-Variants demo.

Please follow the instruction below to install it and run the experiment demo.

Prerequisites

  • Linux (tested on ubuntu 14.04LTS)
  • NVIDIA GPU + CUDA CuDNN (CPU mode and CUDA without CuDNN mode are also available but significantly slower)
  • PyTorch

Getting started

  1. install the dependencies (required by the demo code):

    • tqdm: pip install tqdm
    • pillow: pip install Pillow

  2. clone the pytorch branch:

    # git version must be greater than 1.9.10
git clone https://github.com/ZhouYanzhao/ORN.git -b pytorch --single-branch ORN.pytorch
cd ORN.pytorch
export DIR=$(pwd)

  3. install ORN:

    cd $DIR/install
bash install.sh

  4. run the MNIST-Variants demo:

    cd $DIR/demo
# train ORN on MNIST-rot
python main.py --use-arf
# train baseline CNN
python main.py

Caffe Implementation

The caffe branch contains:

  • the official caffe implementation of ORN (alpha version supports 1x1/3x3 ARFs with 4/8 orientation channels only).
  • the MNIST-Variants demo.

Please follow the instruction below to install it and run the experiment demo.

Prerequisites

  • Linux (tested on ubuntu 14.04LTS)
  • NVIDIA GPU + CUDA CuDNN (CPU mode and CUDA without CuDNN mode are also available but significantly slower)
  • Caffe

Getting started

  1. install the dependency (required by the demo code):

  2. clone the caffe branch:

    # git version must be greater than 1.9.10
git clone https://github.com/ZhouYanzhao/ORN.git -b caffe --single-branch ORN.caffe
cd ORN.caffe
export DIR=$(pwd)

  3. install ORN:

    # modify Makefile.config first
# compile ORN.caffe
make clean && make -j"$(nproc)" all

  4. run the MNIST-Variants demo:

    cd $DIR/examples/mnist
bash get_mnist.sh
# train ORN & CNN on MNIST-rot
bash train.sh

Note

Due to implementation differences,

  • upgrading Conv layers to ORConv layers can be done by adding an orn_param
  • num_output of ORConv layers should be multipied by nOrientation of ARFs

Example:

layer {
  type: "Convolution"
  name: "ORConv" bottom: "Data" top: "ORConv"
  # add this line to replace regular filters with ARFs
  orn_param {orientations: 8}
  param { lr_mult: 1 decay_mult: 2}
  convolution_param {
    # this means 10 ARF feature maps
    num_output: 80
    kernel_size: 3
    stride: 1
    pad: 0
    weight_filler { type: "msra"}
    bias_filler { type: "constant" value: 0}
  }
}

Check the MNIST demo prototxt (and its visualization) for more details.

Citation

If you use the code in your research, please cite:

@INPROCEEDINGS{Zhou2017ORN,
    author = {Zhou, Yanzhao and Ye, Qixiang and Qiu, Qiang and Jiao, Jianbin},
    title = {Oriented Response Networks},
    booktitle = {CVPR},
    year = {2017}
}