Oriented Response Networks

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🎉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:

install the dependencies (required by the demo code):
- torchnet: luarocks install torchnet
- optnet: luarocks install optnet

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)

install ORN:

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

unzip the MNIST dataset:
```
cd $DIR/demo/datasets
unzip MNIST
```

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.

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

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

install the dependencies (required by the demo code):
- tqdm: pip install tqdm
- pillow: pip install Pillow

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)

install ORN:
```
cd $DIR/install
bash install.sh
```

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

install the dependency (required by the demo code):
- idx2numpy: pip install idx2numpy

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)

install ORN:

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

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}
}