DISCONTINUATION OF PROJECT

This project will no longer be maintained by Intel.
Intel has ceased development and contributions including, but not limited to, maintenance, bug fixes, new releases, or updates, to this project.
Intel no longer accepts patches to this project.
If you have an ongoing need to use this project, are interested in independently developing it, or would like to maintain patches for the open source software community, please create your own fork of this project.

TTCF: Training Toolbox for Caffe

This is a BVLC Caffe fork that is intended for deployment multiple SSD-based detection models. It includes

• action detection and action recognition models for smart classroom use-case, see README_AD.md,
• person detection for smart classroom use-case, see README_PD.md,
• face detection model, see README_FD.md,
• person-vehicle-bike crossroad detection model, see README_CR.md,
• age & gender recognition model, see README_AG.md.

Please find original readme file here.

Models

• Action recognition
• Age & gender recognition
• Face detection
• Person detection
• Person-vehicle-bike crossroad detection

Install requirements

1. Install Docker

WARNING Always examine scripts downloaded from the internet before running them locally.

curl -fsSL https://get.docker.com -o get-docker.sh
sudo sh get-docker.sh

2. (optional) Install nvidia-docker plugin

curl -s -L https://nvidia.github.io/nvidia-docker/gpgkey | \
  sudo apt-key add -
distribution=$(. /etc/os-release;echo $ID$VERSION_ID)
curl -s -L https://nvidia.github.io/nvidia-docker/$distribution/nvidia-docker.list | \
  sudo tee /etc/apt/sources.list.d/nvidia-docker.list
sudo apt-get update
sudo apt-get install -y nvidia-docker2
sudo pkill -SIGHUP dockerd

3. (optional) Configure proxy settings Create a file /etc/systemd/system/docker.service.d/proxy.conf that adds the proxy environment variables:

[Service]
Environment="HTTP_PROXY=http://proxy.example.com:80/"
Environment="HTTPS_PROXY=https://proxy.example.com:443/"

Flush changes and restart Docker daemon

sudo systemctl daemon-reload
sudo systemctl restart docker

4. Manage Docker as a non-root user

sudo groupadd docker
sudo usermod -aG docker $USER
# Log out and log back in so that your group membership is re-evaluated.

5. (optional) Verify that nvidia-docker is installed correctly

CUDA_VERSION=$(grep -oP '(?<=CUDA Version )(\d+)' /usr/local/cuda/version.txt)
nvidia-docker run --rm nvidia/cuda:${CUDA_VERSION}.0-cudnn7-devel-ubuntu16.04 nvidia-smi

Build instructions

1. Get the code. We will call the directory that you cloned Caffe into $CAFFE_ROOT

git clone https://github.com/opencv/training_toolbox_caffe.git caffe

2. Download openvino package to root directory of the repository

3. Build docker image

./build_docker_image.sh gpu

Run Docker interactive session

NV_GPU=0 nvidia-docker run --rm --name ttcf -it --user=$(id -u):$(id -g) -v <host_path>:<container_path> ttcf:gpu bash

NOTE To run in CPU mode

./build_docker_image.sh cpu
docker run --rm --name ttcf -it --user=$(id -u):$(id -g) -v <host_path>:<container_path> ttcf:cpu bash

And add to all scripts --gpu -1 --image tccf:cpu arguments.

License and Citation

Original Caffe

Caffe is released under the BSD 2-Clause license. The BAIR/BVLC reference models are released for unrestricted use.

Please cite Caffe in your publications if it helps your research:

@article{jia2014caffe,
  Author = {Jia, Yangqing and Shelhamer, Evan and Donahue, Jeff and Karayev, Sergey and Long, Jonathan and Girshick, Ross and Guadarrama, Sergio and Darrell, Trevor},
  Journal = {arXiv preprint arXiv:1408.5093},
  Title = {Caffe: Convolutional Architecture for Fast Feature Embedding},
  Year = {2014}
}

SSD: Single Shot MultiBox Detector

Please cite SSD in your publications if it helps your research:

@inproceedings{liu2016ssd,
  title = {{SSD}: Single Shot MultiBox Detector},
  author = {Liu, Wei and Anguelov, Dragomir and Erhan, Dumitru and Szegedy, Christian and Reed, Scott and Fu, Cheng-Yang and Berg, Alexander C.},
  booktitle = {ECCV},
  year = {2016}
}

AM-Softmax

If you find AM-Softmax useful in your research, please consider to cite:

@article{Wang_2018_amsoftmax,
  title = {Additive Margin Softmax for Face Verification},
  author = {Wang, Feng and Liu, Weiyang and Liu, Haijun and Cheng, Jian},
  journal = {arXiv preprint arXiv:1801.05599},
  year = {2018}
}

WIDERFace dataset

@inproceedings{yang2016wider,
  Author = {Yang, Shuo and Luo, Ping and Loy, Chen Change and Tang, Xiaoou},
  Booktitle = {IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},
  Title = {WIDER FACE: A Face Detection Benchmark},
  Year = {2016}
}