Install Detection Studio application

The application can be directly downloaded from the repository releases.

To run the app, first give executable permissions running:

    chmod a+x DetectionStudioxxxxx.AppImage

and run it using:

    ./DetectionStudioxxxxx -c configFile

Requirements: python & numpy.

To use Tensorflow and/or Keras, you would need to install them.

To install Tensorflow:

    pip install tensorflow


    pip install tensorflow-gpu

To install Keras:

    pip install keras

To install Pytorch:

    pip install torch

Compile and Install from source

To use the latest version of Detection Studio you need to compile and install it from source. To get started you can either read along or follow these video tutorials.


Common deps

Ubuntu MacOS
sudo apt install build-essential git cmake rapidjson-dev libssl-dev
sudo apt install libboost-dev libboost-filesystem-dev libboost-system-dev libboost-program-options-dev
sudo easy_install numpy
brew install cmake boost rapidjson
Ubuntu MacOS
sudo apt install libgoogle-glog-dev libyaml-cpp-dev qt5-default libqt5svg5-dev brew install glog yaml-cpp qt
Also, just add qt in your PATH by running:
echo 'export PATH="/usr/local/opt/qt/bin:$PATH"' >> ~/.bash_profile

OpenCV 4.2 (with CUDA GPU support)

If you don’t need GPU support (only applicable for Darknet YOLO with OpenCV), just ignore cmake options related with CUDA and GPU.

Ubuntu MacOS
cd ~
wget -O
wget -O
unzip <br> unzip
mv opencv-4.2.0 opencv
mv opencv_contrib-4.2.0 opencv_contrib
brew install opencv
cd ~/opencv
mkdir build
cd build
You must change CUDA_ARCH_BIN version to yours GPU architecture version.
make -j8  
sudo make install  

Reference: How to use OpenCV DNN module with Nvdia GPUs, CUDA and CUDNN

Optional Dependencies

CUDA (For GPU support)

    NVIDIA_GPGKEY_SUM=d1be581509378368edeec8c1eb2958702feedf3bc3d17011adbf24efacce4ab5 && \

    NVIDIA_GPGKEY_FPR=ae09fe4bbd223a84b2ccfce3f60f4b3d7fa2af80 && \
        sudo apt-key adv --fetch-keys && \
        sudo apt-key adv --export --no-emit-version -a $NVIDIA_GPGKEY_FPR | tail -n +5 > && \
        echo "$NVIDIA_GPGKEY_SUM" | sha256sum -c --strict - && rm && \

    sudo sh -c 'echo "deb /" > /etc/apt/sources.list.d/cuda.list' && \
    sudo sh -c 'echo "deb /" > /etc/apt/sources.list.d/nvidia-ml.list'

Update and install

    sudo apt-get update
    sudo apt-get install -y cuda

Below is a list of more optional dependencies you may require depending on your usage.

  • Camera Streaming Support

    Detection Studio can currently read ROS and ICE Camera Streams. So, to enable Streaming support, install any one of them.

  • Inferencing Frameworks

    Detection Studio currently supports many Inferencing Frameworks namely Darknet, TensorFlow, Keras, PyTorch and Caffe. Each one of them has some dependencies, and are mentioned below.

    Choose your favourite one and go ahead.

    • Darknet (jderobot fork)

      Included in OpenCV libraries.

    • TensorFlow

      The only dependency for using TensorFlow as an inferencing framework is TensorFlow. So, just install TensorFlow. It should be 1.4.1 or greater.

    • Keras

      Similarly, the only dependency for using Keras as an inferencing framework is Keras.

    • Caffe

      To use Caffe as an inferencing framework, it is necessary to install OpenCV.

Note: Be Sure to checkout functionality for tutorials on how to use the above mentioned functionalities and frameworks.

How to compile Detection Studio:

Once you have all the required dependencies installed just run:

    git clone
    cd DetectionStudio/DetectionStudio
    mkdir build && cd build
    cmake ..

Note: GPU support is enabled by default

    make -j4

Once it is built, you will find various executables in different folders ready to be executed :smile:.

Starting with Detection Studio

The best way to start is with our beginner’s tutorial for Detection Studio.

If you have any issue feel free to drop a mail or create an issue for the same.