jin-yc10 / Deformable-ConvNets

Deformable Convolutional Networks

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Deformable Convolutional Networks

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

A third-party improvement of Deformable R-FCN + Soft NMS, best single-model performance on COCO detection


Deformable ConvNets is initially described in an ICCV 2017 oral paper.

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


This is an official implementation for Deformable Convolutional Networks (Deformable ConvNets) based on MXNet. 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%).
  • This repository used code from MXNet rcnn example and mx-rfcn.


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

Citing Deformable ConvNets

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

    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}
    Author = {Jifeng Dai, Yi Li, Kaiming He, Jian Sun},
    Title = {{R-FCN}: Object Detection via Region-based Fully Convolutional Networks},
    Conference = {NIPS},
    Year = {2016}

Main Results

training data testing data mAP@0.5 mAP@0.7 time
R-FCN, ResNet-v1-101 VOC 07+12 trainval VOC 07 test 79.6 63.1 0.16s
Deformable R-FCN, ResNet-v1-101 VOC 07+12 trainval VOC 07 test 82.3 67.8 0.19s
training data testing data mAP mAP@0.5 mAP@0.75 mAP@S mAP@M mAP@L
R-FCN, ResNet-v1-101 coco trainval coco test-dev 32.1 54.3 33.8 12.8 34.9 46.1
Deformable R-FCN, ResNet-v1-101 coco trainval coco test-dev 35.7 56.8 38.3 15.2 38.8 51.5
Faster R-CNN (2fc), ResNet-v1-101 coco trainval coco test-dev 30.3 52.1 31.4 9.9 32.2 47.4
Deformable Faster R-CNN (2fc),
coco trainval coco test-dev 35.0 55.0 38.3 14.3 37.7 52.0
training data testing data mIoU time
DeepLab, ResNet-v1-101 Cityscapes train Cityscapes val 70.3 0.51s
Deformable DeepLab, ResNet-v1-101 Cityscapes train Cityscapes val 75.2 0.52s
DeepLab, ResNet-v1-101 VOC 12 train (augmented) VOC 12 val 70.7 0.08s
Deformable DeepLab, ResNet-v1-101 VOC 12 train (augmented) VOC 12 val 75.9 0.08s

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

Requirements: Software

  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 2.7. We recommend using Anaconda2 as it already includes many common packages. We do not support Python 3 yet, if you want to use Python 3 you need to modify the code to make it work.

  3. 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 -r requirements.txt
  4. For Windows users, Visual Studio 2015 is needed to compile cython module.

Requirements: Hardware

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


  1. Clone the Deformable ConvNets repository, and we'll call the directory that you cloned Deformable-ConvNets as ${DCN_ROOT}.
git clone https://github.com/msracver/Deformable-ConvNets.git
  1. 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.

  2. Install MXNet:

    Quick start

    3.1 Install MXNet and all dependencies by

    pip install -r requirements.txt

    If there is no other error message, MXNet should be installed successfully. If you want to build MXNet from source, please follow 3.2 - 3.7.

    Build from source (alternative way)

    3.2 Checkout this repo to Deformable-ConvNets@(commit ecc484f) by

    cd $(DCN_ROOT)
    git checkout ecc484f

    3.3 Clone MXNet and checkout to MXNet@(commit 62ecb60) by

    git clone --recursive https://github.com/dmlc/mxnet.git
    git checkout 62ecb60
    git submodule update

    3.4 Copy operators in $(DCN_ROOT)/rfcn/operator_cxx or $(DCN_ROOT)/faster_rcnn/operator_cxx to $(YOUR_MXNET_FOLDER)/src/operator/contrib by

    cp -r $(DCN_ROOT)/rfcn/operator_cxx/* $(MXNET_ROOT)/src/operator/contrib/

    3.5 Compile MXNet

    cd ${MXNET_ROOT}
    make -j $(nproc) USE_OPENCV=1 USE_BLAS=openblas USE_CUDA=1 USE_CUDA_PATH=/usr/local/cuda USE_CUDNN=1

    3.6 Install the MXNet Python binding by

    Note: If you will actively switch between different versions of MXNet, please follow 3.7 instead of 3.6

    cd python
    sudo python setup.py install

    3.7 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.

  3. For Deeplab, we use the argumented VOC 2012 dataset. The argumented annotations are provided by SBD dataset. For convenience, we provide the converted PNG annotations and the lists of train/val images, please download them from OneDrive.

Demo & Deformable Model

We provide trained deformable convnet models, including the deformable R-FCN & Faster R-CNN models trained on COCO trainval, and the deformable DeepLab model trained on CityScapes train.

  1. To use the demo with our pre-trained deformable models, please download manually from OneDrive, and put it under folder model/.

    Make sure it looks like this:

  2. To run the R-FCN 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
  3. To run the DeepLab demo, run

    python ./deeplab/demo.py

    By default it will run Deformable Deeplab and gives several prediction results, to run DeepLab, use

    python ./deeplab/demo.py --deeplab_only
  4. To visualize the offset of deformable convolution and deformable psroipooling, run

    python ./rfcn/deform_conv_demo.py
    python ./rfcn/defrom_psroi_demo.py

Preparation for Training & Testing

For R-FCN/Faster R-CNN:

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

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


For DeepLab:

  1. Please download Cityscapes and VOC 2012 datasets and make sure it looks like this:

  2. Please download argumented VOC 2012 annotations/image lists, and put the argumented annotations and the argumented train/val lists into:


    , Respectively.

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



  1. All of our experiment settings (GPU #, dataset, etc.) are kept in yaml config files at folder ./experiments/rfcn/cfgs, ./experiments/faster_rcnn/cfgs and ./experiments/deeplab/cfgs/.

  2. Eight config files have been provided so far, namely, R-FCN for COCO/VOC, Deformable R-FCN for COCO/VOC, Faster R-CNN(2fc) for COCO/VOC, Deformable Faster R-CNN(2fc) for COCO/VOC, Deeplab for Cityscapes/VOC and Deformable Deeplab for Cityscapes/VOC, respectively. We use 8 and 4 GPUs to train models on COCO and on VOC for R-FCN, respectively. For deeplab, we use 4 GPUs for all experiments.

  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


Q: It says AttributeError: 'module' object has no attribute 'DeformableConvolution'.

A: This is because either

  • you forget to copy the operators to your MXNet folder

  • or you copy to the wrong path

  • or you forget to re-compile

  • or you install the wrong MXNet

    Please print mxnet.__path__ to make sure you use correct MXNet

Q: I encounter segment fault at the beginning.

A: A compatibility issue has been identified between MXNet and opencv-python 3.0+. We suggest that you always import cv2 first before import mxnet in the entry script.

Q: I find the training speed becomes slower when training for a long time.

A: It has been identified that MXNet on Windows has this problem. So we recommend to run this program on Linux. You could also stop it and resume the training process to regain the training speed if you encounter this problem.

Q: Can you share your caffe implementation?

A: Due to several reasons (code is based on a old, internal Caffe, port to public Caffe needs extra work, time limit, etc.). We do not plan to release our Caffe code. Since current MXNet convolution implementation is very similar to Caffe (almost the same), it is easy to port to Caffe by yourself, the core CUDA code could be kept unchanged. Anyone who wish to do it is welcome to make a pull request.

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Deformable Convolutional Networks

License:Apache License 2.0


Language:Python 73.5%Language:Cuda 20.0%Language:C++ 6.0%Language:C 0.5%Language:Makefile 0.0%Language:Batchfile 0.0%Language:Shell 0.0%