Accel is a fast, high accuracy video segmentation system, initially described in a CVPR 2019 paper. Accel is implemented as an extension of Deep Feature Flow, a video recognition framework released by MSR Asia in 2017.

Some notes:

• Accel combines optical flow-based keyframe feature warping (Deep Feature Flow) with per-frame temporal correction (DeepLab) in a score fusion step to improve on the accuracy of each of its constituent sub-networks.
• Accel is trained end-to-end on the task of video semantic segmentation.
• Accel can be built with a range of feature sub-networks, from ResNet-18 to ResNet-101. Accel based on ResNet-18 (Accel-18) is fast and reasonably accurate. Accel based on ResNet-101 (Accel-101) exceeds state-of-the-accuracy.
• Accel can be evaluated on sparsely annotated video recognition datasets, such as Cityscapes and CamVid.

Example visualizations

      Deep Feature Flow 		DeepLab-18 			Accel-18

This is an official implementation for Accel in MXNet. It is worth noting that:

• The code is tested on official MXNet@(commit 62ecb60).
• 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 R-CNN example and MX-RFCN.

© UC Berkeley and Microsoft, 2019. Licensed under the MIT License.

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

@inproceedings{jain19,
    Author = {Samvit Jain, Xin Wang, Joseph E. Gonzalez},
    Title = {Accel: A Corrective Fusion Network for Efficient Semantic Segmentation on Video},
    Conference = {CVPR},
    Year = {2019}
}

@inproceedings{zhu17dff,
    Author = {Xizhou Zhu, Yuwen Xiong, Jifeng Dai, Lu Yuan, Yichen Wei},
    Title = {Deep Feature Flow for Video Recognition},
    Conference = {CVPR},
    Year = {2017}
}

Running time is benchmarked on a single GPU (mini-batch size 1, key-frame duration length 5).

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

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

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

1. Clone the Accel repository. Let ${ACCEL_ROOT} denote the cloned repository.

git clone https://github.com/SamvitJ/Accel.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. 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 $(ACCEL_ROOT)/dff_rfcn/operator_cxx or $(ACCEL_ROOT)/rfcn/operator_cxx to $(YOUR_MXNET_FOLDER)/src/operator/contrib by

   cp -r $(ACCEL_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_deeplab/cfgs/*.yaml to $(YOUR_MXNET_PACKAGE). Thus you can switch among different versions of MXNet quickly.

1. Please download the Cityscapes dataset. Specifically, make sure to download the leftImg8bit_sequence_trainvaltest.zip package (324GB), which contains 30-frame snippets for each Cityscapes train / val / test example. This is a requirement for testing at keyframe intervals > 1.

   In addition, download gtFine_trainvaltest.zip (241MB), which contains ground truth annotations.

   Place the data in the data folder under the root directory:

   ./data/cityscapes
   ./data/cityscapes/leftImg8bit_sequence/train
   ./data/cityscapes/leftImg8bit_sequence/val
   ./data/cityscapes/leftImg8bit_sequence/test
   ./data/cityscapes/gtFine/train
   ./data/cityscapes/gtFine/val
   ./data/cityscapes/gtFine/test
   

1. To evaluate Accel using our models, please download the following models, and place them under folder model/:

   • Base DFF model (with FlowNet) -- manually from OneDrive (for users in Mainland China, please try Baidu Yun)
   • Accel models -- manually from Google Drive

   Make sure the directory looks like this:

   ./model/rfcn_dff_flownet_vid-0000.params
   ./model/accel-18-0000.params
   ./model/accel-34-0000.params
   ./model/accel-50-0000.params
   ./model/accel-101-0000.params
   

2. Edit dff_deeplab/demo.py to set path_demo_data and path_demo_labels. These should specify the path to the parent directories housing the Cityscapes sequence data and labels on your disk. For example:

   path_demo_data = '/ebs/Accel/data/cityscapes/'
   path_demo_labels = '/ebs/Accel/data/cityscapes/'
   

3. Run one of the following commands to evaluate Accel-x on the Cityscapes val data.

   Default setting (Accel version 18, keyframe interval 1, num examples 10):

   python ./dff_deeplab/demo.py
   

   Custom setting (Accel version X, keyframe interval Y, num examples Z):

   python ./dff_deeplab/demo.py --version 50 --interval 5 --num_ex 100
   

   See dff_deeplab/demo.py for other configurable runtime settings.

1. Please download the following models, and place them under folder ./model:

   • Base DFF model (with FlowNet) -- manually from OneDrive (for users in Mainland China, please try Baidu Yun)
   • DeepLab models -- manually from Google Drive

   Make sure the directory looks like this:

   ./model/rfcn_dff_flownet_vid-0000.params
   ./model/pretrained/deeplab-18-0000.params
   ./model/pretrained/deeplab-34-0000.params
   ./model/pretrained/deeplab-50-0000.params
   ./model/pretrained/deeplab-101-0000.params
   

2. Identify the config file for the model you wish to train. The config files are located under the directory experiments/dff_deeplab/cfgs/, and contain the experiment blueprints for training particular models (e.g. Accel-18) on particular datasets (e.g. Cityscapes).

3. Edit the appropriate config file (e.g. accel_18_cityscapes_end2end_ohem.yaml) to specify the GPU ids available for training on your machine. For example, if you are training on p2.8xlarge (an Amazon EC2 instance with 8 GPUs), set:

   gpus: '0,1,2,3,4,5,6,7'
   

4. To train Accel, use the following command. For example, to train Accel-18 on Cityscapes, run:

   python experiments/dff_deeplab/dff_deeplab_end2end_train_test.py \
   	--cfg experiments/dff_deeplab/cfgs/accel_18_cityscapes_end2end_ohem.yaml
   

   The training log and model checkpoints will be saved under output/dff_deeplab/cityscapes/.

1. All of our experiment settings (GPU ids, dataset config, training config, etc.) are specified in yaml config files at folder ./experiments/{deeplab,dff_deeplab}/cfgs.

2. Please refer to our code and config files for more details.

Accel was tested on:

• Ubuntu 16.04 with 1,8 Nvidia Tesla K80 GPUs

Deep Feature Flow was tested on:

• 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

For common errors, please see the Deep Feature Flow FAQ.