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.

Introduction

Deformable ConvNets is initially described in an arxiv tech report.

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

Disclaimer

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.

License

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

Citing Deformable ConvNets

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}
}

Main Results

	training data	testing data	[email protected]	[email protected]	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	[email protected]	[email protected]	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

	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 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.

Requirements: Hardware

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

Installation

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.
5. 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 list of training/validation images, please download them from OneDrive.

Demo

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
   ./model/deeplab_dcn_cityscapes-0000.params
   ./model/deeplab_cityscapes-0000.params
   

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
   

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

Preparation for Training & Testing

For R-FCN:

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
   

For DeepLab:

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

   ./data/cityscapes/
   ./data/VOCdevkit/VOC2012/
   

2. Please download argumented VOC 2012, and put the argumented annotations and the argumented training/validation list into:

   ./data/VOCdevkit/VOC2012/SegmentationClass/
   ./data/VOCdevkit/VOC2012/ImageSets/Main/
   

   , 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:

   ./model/pretrained_model/resnet_v1_101-0000.params
   

Usage

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

Misc.

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