Deep Feature Flow for Video Recognition

The major contributors of this repository include Xizhou Zhu, Yuwen Xiong, Jifeng Dai, Lu Yuan, and Yichen Wei.

Introduction

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:

Deep Feature Flow significantly speeds up video recognition by applying the heavy-weight image recognition network (e.g., ResNet-101) on sparse key frames, and propagating the recognition outputs (feature maps) to the other frames by the light-weight flow network (e.g., FlowNet).
The entire system is end-to-end trained for the task of video recognition, which is vital for improving the recognition accuracy. Directly adopting state-of-the-art flow estimation methods without end-to-end training would deliver noticable worse results.

Click image to watch our demo video

Disclaimer

This is an official implementation for Deep Feature Flow for Video Recognition (DFF) 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 Deep Feature Flow.
We trained our model based on the ImageNet pre-trained ResNet-v1-101 model and Flying Chairs pre-trained FlowNet model using a model converter. The converted ResNet-v1-101 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

Citing Deep Feature Flow

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}
}

Main Results

	_{training data}	_{testing data}	_mAP@0.5	_{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.

Requirements: Software

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 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.

Requirements: Hardware

Any NVIDIA GPUs with at least 6GB memory should be OK

Installation

Clone the Deep Feature Flow repository

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.
Copy operators in ./rfcn/operator_cxx to $(YOUR_MXNET_FOLDER)/src/operator/contrib and recompile MXNet.
Please install MXNet following the official guide of MXNet. For advanced users, you may put your Python packge into ./external/mxnet/$(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.

Demo

To run the demo with our trained model (on ImageNet DET + VID train), please download the model manually from OneDrive, 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

Preparation for Training & Testing

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, 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
```

Usage

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.

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

TuSimple/Deep-Feature-Flow-MSRA