Deep Learning Studio

Information regarding this Repository

This repository contains the deep learning regression and classification models for all robots used in the JdeRobot community.

Structure of the branch

├── Carla-FollowLane
|   |
|   |── pytorch
|   |   
|   |── tensorflow
|   |   
├── Carla-FollowLaneTraffic
|   |
|   |── pytorch
|   |  
├── Formula1-FollowLine
|   |
|   |── pytorch
|   |   |── PilotNet                                # Pilot Net pytorch implementation
|   |   |   ├── scripts                             # scripts for running experiments 
|   |   |   ├── utils                               
|   |   |   |   ├── pilot_net_dataset.py            # Torchvision custom dataset
|   |   |   |   ├── pilotnet.py                     # CNN for PilotNet
|   |   |   |   ├── transform_helpers.py            # Data Augmentation
|   |   |   |   └── processing.py                   # Data collecting, processing and utilities
|   |   |   └── train.py                            # training code
|   |   |
|   |   └── PilotNetStacked                         # Pilot Net Stacked Image implementation
|   |       ├── scripts                             # scripts for running experiments 
|   |       ├── utils                               
|   |       |   ├── pilot_net_dataset.py            # Sequentially stacked image dataset
|   |       |   ├── pilotnet.py                     # Modified Hyperparams 
|   |       |   ├── transform_helpers.py            # Data Augmentation
|   |       |   └── processing.py                   # Data collecting, processing and utilities
|   |       └── train.py                            # training code
|   |
|   ├── tensoflow
|       |── PilotNet                                # Pilot Net tensorflow implementation
|           ├── utils                               
|           |   ├── dataset.py                      # Custom dataset
|           |   ├── pilotnet.py                     # CNN for PilotNet
|           |   └── processing.py                   # Data collecting, processing and utilities
|           └── train.py                            # training code
├── Drone-FollowLine
    |
    |── DeepPilot                               # DeepPilot CNN pytorch implementation
    |   ├── scripts                             # scripts for running experiments 
    |   ├── utils                               
    |   |   ├── pilot_net_dataset.py            # Torchvision custom dataset
    |   |   ├── pilotnet.py                     # CNN for DeepPilot
    |   |   ├── transform_helpers.py            # Data Augmentation
    |   |   └── processing.py                   # Data collecting, processing and utilities
    |   └── train.py                            # training code

Setting up this branch

First, install Python 3.10

sudo apt install software-properties-common -y
sudo add-apt-repository ppa:deadsnakes/ppa
sudo apt install python3.10
sudo apt install python3.10-venv
sudo apt install python3.10-dev
sudo apt install python3.10-minimal
sudo apt install python3.10-distutils

Next, it is best to setup a virtual environment with python 3.10

cd ~ && mkdir pyenvs && cd pyenvs
python3.10 -m venv dlstudio
source ~/pyenvs/dlstudio/bin/activate
python3 -m pip install -U pip

cd ~
git clone https://github.com/JdeRobot/DeepLearningStudio DeepLearningStudio
cd DeepLearningStudio
pip install -r requirements.txt

References

1. Bojarski, Mariusz, Davide Del Testa, Daniel Dworakowski, Bernhard Firner, Beat Flepp, Prasoon Goyal, Lawrence D. Jackel et al. "End to end learning for self-driving cars." arXiv preprint arXiv:1604.07316 (2016). https://arxiv.org/abs/1604.07316

@article{bojarski2016end,
  title={End to end learning for self-driving cars},
  author={Bojarski, Mariusz and Del Testa, Davide and Dworakowski, Daniel and Firner, Bernhard and Flepp, Beat and Goyal, Prasoon and Jackel, Lawrence D and Monfort, Mathew and Muller, Urs and Zhang, Jiakai and others},
  journal={arXiv preprint arXiv:1604.07316},
  year={2016}
}

@article{bojarski2017explaining,
  title={Explaining how a deep neural network trained with end-to-end learning steers a car},
  author={Bojarski, Mariusz and Yeres, Philip and Choromanska, Anna and Choromanski, Krzysztof and Firner, Bernhard and Jackel, Lawrence and Muller, Urs},
  journal={arXiv preprint arXiv:1704.07911},
  year={2017}
}

2. Rojas-Perez, L.O., & Martinez-Carranza, J. (2020). DeepPilot: A CNN for Autonomous Drone Racing. Sensors, 20(16), 4524. https://doi.org/10.3390/s20164524

@article{rojas2020deeppilot,
  title={DeepPilot: A CNN for Autonomous Drone Racing},
  author={Rojas-Perez, Leticia Oyuki and Martinez-Carranza, Jose},
  journal={Sensors},
  volume={20},
  number={16},
  pages={4524},
  year={2020},
  publisher={Multidisciplinary Digital Publishing Institute}
}

3. Paniego, Sergio and Paliwal, Nikhil and Cañas, JoséMaría (2023). DeepPilot: Model Optimization in Deep Learning Based Robot Control for Autonomous Driving. https://doi.org/10.1109/LRA.2023.3336244

@article{Paniego2023,
    author={Paniego, Sergio and Paliwal, Nikhil and Cañas, JoséMaría},
    journal={IEEE Robotics and Automation Letters}, 
    title={Model Optimization in Deep Learning Based Robot Control for Autonomous Driving}, 
    year={2024},
    volume={9},
    number={1},
    pages={715-722},
    doi={10.1109/LRA.2023.3336244}
}