The major contributors of this repository include Yuwen Xiong, Haozhi Qi, Guodong Zhang, Yi Li, Jifeng Dai, Bin Xiao and Yichen Wei.

This is an official implementation for Deformable Convolutional Networks (Deformable ConvNets). It is worth noticing that:

• The original implementation is based on our internal Caffe version on Windows. There are slight differences in the final accuracy and running time due to the plenty details in platform switch.
• The code is tested on official MXNet@(commit 62ecb60) with the extra operators for Deformable ConvNets.
• We trained our model based on the ImageNet pre-trained ResNet-v1-101 using a model converter. The converted model produces slightly lower accuracy (Top-1 Error on ImageNet val: 24.0% v.s. 23.6%).
• By now it only contains Deformable ConvNets with R-FCN. Deformable ConvNets with DeepLab will be released soon.
• This repository used code from MXNet rcnn example and mx-rfcn.

Deformable ConvNets is initially described in an arxiv tech report.

R-FCN is initially described in a NIPS 2016 paper.

© Microsoft, 2017. Licensed under an Apache-2.0 license.

If you find Deformable ConvNets useful in your research, please consider citing:

@article{dai17dcn,
    Author = {Jifeng Dai, Haozhi Qi, Yuwen Xiong, Yi Li, Guodong Zhang, Han Hu, Yichen Wei},
    Title = {Deformable Convolutional Networks},
    Journal = {arXiv preprint arXiv:1703.06211},
    Year = {2017}
}
@inproceedings{dai16rfcn,
    Author = {Jifeng Dai, Yi Li, Kaiming He, Jian Sun},
    Title = {{R-FCN}: Object Detection via Region-based Fully Convolutional Networks},
    Conference = {NIPS},
    Year = {2016}
}

Running time is counted on a single Maxwell Titan X GPU (mini-batch size is 1 in inference).

1. MXNet from the offical repository. We tested our code on MXNet@(commit 62ecb60). Due to the rapid development of MXNet, it is recommended to checkout this version if you encounter any issues. We may maintain this repository periodically if MXNet adds important feature in future release.

2. Python packages might missing: cython, opencv-python >= 3.2.0, easydict. If pip is set up on your system, those packages should be able to be fetched and installed by running

   pip install Cython
   pip install opencv-python==3.2.0.6
   pip install easydict==1.6
   

3. For Windows users, Visual Studio 2015 is needed to compile cython module.

Any NVIDIA GPUs with at least 4GB memory should be OK.

1. Clone the Deformable ConvNets repository

git clone https://github.com/msracver/Deformable-ConvNets.git

2. For Windows users, run cmd .\init.bat. For Linux user, run sh ./init.sh. The scripts will build cython module automatically and create some folders.
3. Copy operators in ./rfcn/operator_cxx to $(YOUR_MXNET_FOLDER)/src/operator/contrib and recompile MXNet.
4. Please install MXNet following the official guide of MXNet. For advanced users, you may put your Python packge into ./external/mxnet/$(YOUR_MXNET_PACKAGE), and modify MXNET_VERSION in ./experiments/rfcn/cfgs/*.yaml to $(YOUR_MXNET_PACKAGE). Thus you can switch among different versions of MXNet quickly.

1. To use the demo with our trained model (on COCO trainval), please download the model manually from OneDrive, and put it under folder model/.

   Make sure it looks like this:

   ./model/rfcn_dcn_coco-0000.params
   ./model/rfcn_coco-0000.params
   

2. To run the demo, run

   python ./rfcn/demo.py
   

   By default it will run Deformable R-FCN and gives several prediction results, to run R-FCN, use

   python ./rfcn/demo.py --rfcn_only
   

We will release the visualizaiton tool which visualizes the deformation effects soon.

1. Please download COCO and VOC 2007+2012 dataset, and make sure it looks like this:

   ./data/coco/
   ./data/VOCdevkit/VOC2007/
   ./data/VOCdevkit/VOC2012/
   

2. Please download ImageNet-pretrained ResNet-v1-101 model manually from OneDrive, and put it under folder ./model. Make sure it looks like this:

   ./model/pretrained_model/resnet_v1_101-0000.params
   

1. All of our experiment settings (GPU #, dataset, etc.) are kept in yaml config files at folder ./experiments/rfcn/cfgs.
2. Four config files have been provided so far, namely, R-FCN for COCO/VOC and Deformable R-FCN for COCO/VOC, respectively. We use 8 and 4 GPUs to train models on COCO and on VOC, respectively.
3. To perform experiments, run the python scripts with the corresponding config file as input. For example, to train and test deformable convnets on COCO with ResNet-v1-101, use the following command

   python experiments\rfcn\rfcn_end2end_train_test.py --cfg experiments\rfcn\cfgs\resnet_v1_101_coco_trainval_rfcn_dcn_end2end_ohem.yaml
   

   A cache folder would be created automatically to save the model and the log under output/rfcn_dcn_coco/.
4. Please find more details in config files and in our code.

Code has been tested under:

• Ubuntu 14.04 with a Maxwell Titan X GPU and Intel Xeon CPU E5-2620 v2 @ 2.10GHz
• Windows Server 2012 R2 with 8 K40 GPUs and Intel Xeon CPU E5-2650 v2 @ 2.60GHz
• Windows Server 2012 R2 with 4 Pascal Titan X GPUs and Intel Xeon CPU E5-2650 v4 @ 2.30GHz