# training_toolbox_caffe

**Repository Path**: openvinotoolkit-prc/training_toolbox_caffe

## Basic Information

- **Project Name**: training_toolbox_caffe
- **Description**: No description available
- **Primary Language**: Unknown
- **License**: Apache-2.0
- **Default Branch**: develop
- **Homepage**: None
- **GVP Project**: No

## Statistics

- **Stars**: 0
- **Forks**: 0
- **Created**: 2020-09-25
- **Last Updated**: 2025-06-26

## Categories & Tags

**Categories**: Uncategorized

**Tags**: None

## README

# 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](https://github.com/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](README_AD.md),
- person detection for smart classroom use-case, see [README_PD.md](README_PD.md),
- face detection model, see [README_FD.md](README_FD.md),
- person-vehicle-bike crossroad detection model, see [README_CR.md](README_CR.md),
- age & gender recognition model, see [README_AG.md](README_AG.md).

Please find original readme file [here](README_BVLC.md).


## Models
* [Action recognition](./README_AD.md)
* [Age & gender recognition](./README_AG.md)
* [Face detection](./README_FD.md)
* [Person detection](./README_PD.md)
* [Person-vehicle-bike crossroad detection](./README_CR.md)


## Install requirements
1. [Install Docker](https://docs.docker.com/install/linux/docker-ce/ubuntu/)

**WARNING** Always examine scripts downloaded from the internet before running them locally.
```Shell
curl -fsSL https://get.docker.com -o get-docker.sh
sudo sh get-docker.sh
```

2. (optional) [Install nvidia-docker plugin](https://github.com/nvidia/nvidia-docker/wiki/Installation-(version-2.0))
```Shell
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](https://docs.docker.com/config/daemon/systemd/#httphttps-proxy)
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]( https://docs.docker.com/engine/installation/linux/linux-postinstall/#manage-docker-as-a-non-root-user)
```Shell
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
```Shell
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`
```Shell
git clone https://github.com/opencv/training_toolbox_caffe.git caffe
```

2. [Download openvino package](https://software.intel.com/en-us/openvino-toolkit) to root directory of the repository

3. Build docker image
```Shell
./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](https://github.com/BVLC/caffe/blob/master/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}
    }