Deep Feature Flow for Video Recognition
MIT License
The major contributors of this repository include Xizhou Zhu, Yuwen Xiong, Jifeng Dai, Lu Yuan, and Yichen Wei.
Deep Feature Flow is initially described in a CVPR 2017 paper. It provides a simple, fast, accurate, and end-to-end framework for video recognition (e.g., object detection and semantic segmentation in videos). It is worth noting that:
Click image to watch our demo video
This is an official implementation for Deep Feature Flow for Video Recognition (DFF) based on MXNet. It is worth noticing that:
© Microsoft, 2018. Licensed under the MIT License.
If you find Deep Feature Flow useful in your research, please consider citing:
@inproceedings{zhu17dff,
Author = {Xizhou Zhu, Yuwen Xiong, Jifeng Dai, Lu Yuan, Yichen Wei},
Title = {Deep Feature Flow for Video Recognition},
Conference = {CVPR},
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}
}
training data | testing data | [email protected] | time/image (Tesla K40) | time/image(Maxwell Titan X) | |
---|---|---|---|---|---|
Frame baseline(R-FCN, ResNet-v1-101) | ImageNet DET train + VID train | ImageNet VID validation | 74.1 | 0.271s | 0.133s |
Deep Feature Flow(R-FCN, ResNet-v1-101, FlowNet) | ImageNet DET train + VID train | ImageNet VID validation | 73.0 | 0.073s | 0.034s |
Running time is counted on a single GPU (mini-batch size is 1 in inference, key-frame duration length for Deep Feature Flow is 10).
The runtime of the light-weight FlowNet seems to be a bit slower on MXNet than that on Caffe.
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.
Python 2.7. We recommend using Anaconda2 as it already includes many common packages. We do not suppoort Python 3 yet, if you want to use Python 3 you need to modify the code to make it work.
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
For Windows users, Visual Studio 2015 is needed to compile cython module.
Any NVIDIA GPUs with at least 6GB memory should be OK
git clone https://github.com/msracver/Deep-Feature-Flow.git
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.
Install MXNet:
3.1 Clone MXNet and checkout to MXNet@(commit 62ecb60) by
git clone --recursive https://github.com/dmlc/mxnet.git
git checkout 62ecb60
git submodule update
3.2 Copy operators in $(DFF_ROOT)/dff_rfcn/operator_cxx
or $(DFF_ROOT)/rfcn/operator_cxx
to $(YOUR_MXNET_FOLDER)/src/operator/contrib
by
cp -r $(DFF_ROOT)/dff_rfcn/operator_cxx/* $(MXNET_ROOT)/src/operator/contrib/
3.3 Compile MXNet
cd ${MXNET_ROOT}
make -j4
3.4 Install the MXNet Python binding by
Note: If you will actively switch between different versions of MXNet, please follow 3.5 instead of 3.4
cd python
sudo python setup.py install
3.5 For advanced users, you may put your Python packge into ./external/mxnet/$(YOUR_MXNET_PACKAGE)
, and modify MXNET_VERSION
in ./experiments/dff_rfcn/cfgs/*.yaml
to $(YOUR_MXNET_PACKAGE)
. Thus you can switch among different versions of MXNet quickly.
To run the demo with our trained model (on ImageNet DET + VID train), please download the model manually from OneDrive (for users from Mainland China, please try Baidu Yun), and put it under folder model/
.
Make sure it looks like this:
./model/rfcn_vid-0000.params
./model/rfcn_dff_flownet_vid-0000.params
Run (inference batch size = 1)
python ./rfcn/demo.py
python ./dff_rfcn/demo.py
or run (inference batch size = 10)
python ./rfcn/demo_batch.py
python ./dff_rfcn/demo_batch.py
Please download ILSVRC2015 DET and ILSVRC2015 VID dataset, and make sure it looks like this:
./data/ILSVRC2015/
./data/ILSVRC2015/Annotations/DET
./data/ILSVRC2015/Annotations/VID
./data/ILSVRC2015/Data/DET
./data/ILSVRC2015/Data/VID
./data/ILSVRC2015/ImageSets
Please download ImageNet pre-trained ResNet-v1-101 model and Flying-Chairs pre-trained FlowNet model manually from OneDrive (for users from Mainland China, please try Baidu Yun), and put it under folder ./model
. Make sure it looks like this:
./model/pretrained_model/resnet_v1_101-0000.params
./model/pretrained_model/flownet-0000.params
All of our experiment settings (GPU #, dataset, etc.) are kept in yaml config files at folder ./experiments/{rfcn/dff_rfcn}/cfgs
.
Two config files have been provided so far, namely, Frame baseline with R-FCN and Deep Feature Flow with R-FCN for ImageNet VID. We use 4 GPUs to train models on ImageNet VID.
To perform experiments, run the python script with the corresponding config file as input. For example, to train and test Deep Feature Flow with R-FCN, use the following command
python experiments/dff_rfcn/dff_rfcn_end2end_train_test.py --cfg experiments/dff_rfcn/cfgs/resnet_v1_101_flownet_imagenet_vid_rfcn_end2end_ohem.yaml
A cache folder would be created automatically to save the model and the log under output/dff_rfcn/imagenet_vid/
.
Please find more details in config files and in our code.
Code has been tested under:
Q: It says AttributeError: 'module' object has no attribute 'MultiProposal'
.
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 and install
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 a warping layer is easy to implement, anyone who wish to do it is welcome to make a pull request.