Installation and requirements

Requirements v4 has been tested in computers with the following specifications:

  • Operating system: 64bit GNU/linux Mint 19.1/20.2, Ubuntu 18.4 and Windows 10.

  • CPU: Core(TM) i7-7700K CPU @4.20GHz 6 core Intel(R) or Core(TM) i7-6800K CPU @3.40GHz 4 core

  • GPU: Nvidia TITAN X, GeForce GTX 1080 Ti, GeForce GTX 1060, 1070 and 1080.

  • RAM: 16Gb-128Gb.

  • Disk: 1TB SSD is coded in python 3.7 and uses PyTorch libraries and OpenCV (version 3). Due to the intense use of deep neural networks, we recommend using a computer with a dedicated NVIDA GPU supporting compute capability 3.0 or higher. Note that the parts of the algorithm using Tensorflow libraries will run faster with a GPU.

Pre-installation checks

Install NVIDIA drivers +410.38 (for the installation with GPU support)

Install with GPU support in your computer if you want to track videos keeping the identities of each animal. Note that allows users to track single animals and to track groups of animals without keeping the identity. For these cases you do not need GPU support (see the Option 3 in the installation instructions below). v4 has been tested on PyTorch 1.10 and cudatoolkit 10.2 and 11.3. Before installing check which NVIDIA driver you have installed and its compatibility with the corresponding CUDA toolkit version (see cuda compatiblity <>).

Below we give instructions to check your NVIDIA driver version and how to install a compatible version with CUDA 10.2 or 11.3.

For Linux users

To check whether the NVIDIA drivers are correctly installed in your computer, open a terminal and type:


You should get an output similar to this one

| NVIDIA-SMI 495.44       Driver Version: 495.44       CUDA Version: 11.5     |
| GPU  Name        Persistence-M| Bus-Id        Disp.A | Volatile Uncorr. ECC |
| Fan  Temp  Perf  Pwr:Usage/Cap|         Memory-Usage | GPU-Util  Compute M. |
|                               |                      |               MIG M. |
|   0  NVIDIA GeForce ...  Off  | 00000000:01:00.0  On |                  N/A |
| N/A   56C    P8     5W /  N/A |    167MiB /  8111MiB |      0%      Default |
|                               |                      |                  N/A |

| Processes:                                                                  |
|  GPU   GI   CI        PID   Type   Process name                  GPU Memory |
|        ID   ID                                                   Usage      |
|    0   N/A  N/A      1325      G   /usr/lib/xorg/Xorg                 87MiB |
|    0   N/A  N/A      2898      G   ...AAAAAAAAA= --shared-files       77MiB |

Check that in the part where it says “Driver Version” you have value higher than 440.33 (compatible with CUDA 10.2) or 450.80.02 (compatible with CUDA 11.3).

If you fail to get this output or your version is smaller than 440.33, then you will need to instal or update your nvidia drivers.

> NOTE: this link <> > has nice instructions to get the latest NVIDIA drivers either using your Update Manager or the terminal.

  1. Clean the system of other Nvidia drivers

sudo apt-get purge nvidia*
  1. Check which is the latest driver version system in this link.

  2. Update and upgrade your system:

sudo apt update
sudo apt upgrade
  1. Check which is the latest available version of the NVIDIA drivers for your system:

apt search nvidia-driver
  1. Install the NVIDIA GPU driver. In the following command, substitute the XXX by the number of the driver you want to install (e.g. nvidia-driver-495).

sudo apt-get install nvidia-driver-XXX
  1. Reboot the system.

sudo reboot
  1. Check the installation.


For Windows users

To check which NVIDIA drivers you have installed in your computer following these steps (adapted from this page):

  1. Right click any empty area on your desktop screen, and select NVIDIA Control Panel.

  2. Click System Information (on the bottom left corner) to open the driver information.

  3. Check the Driver version in the Details section.

You can download the latest driver available for your GPU from the NVIDIA webpage.

After downloading the .exe file, execute it and follow the instructions. After the installation you will be asked to reboot the computer, please do so for the installation to be complete.

> NOTE: For Windows you will need an NVIDIA driver >=441.22 for CUDA 10.2 and >=456.38 for CUDA 11.3.

Preparing a Conda environment (for Linux and Windows)

It is good practice to install python packages in virtual environments. In particular, we recommend using Conda virtual environments. Find here the Conda installation instructions for Linux and Windows.

When deciding whether to install Anaconda or Miniconda, you can find some information about the differences here. For simplicity, we recommend installing Miniconda.

From now on, every time we refer to the terminal, Linux users are meant to use the command line and Windows user are meant to use the Anaconda Powershell Prompt that it is installed when installing Miniconda or Anaconda.

To check whether the Conda package manager is installed, you can open a terminal and type


if you get the following output

conda: command not found

Miniconda is not installed in your system. Follow the instructions in the link above to install it.

Create a Conda environment where will be installed.

conda create -n idtrackerai python=3.7

You can learn more about managing Conda environments in this link.

Once the Conda environment has been create you should be able to activate it doing

conda activate idtrackerai


source activate idtrackerai.


Assuming that you have the latest version of the NVIDIA drivers installed, and Anaconda or Miniconda installed, the recomended way to install v4 is using the following commands (to be run in a linux terminal or in the Anaconda Powershell Prompt in Windows):

conda create -n idtrackerai python=3.7
conda activate idtrackerai
pip install idtrackerai[gui]
conda install pytorch torchvision -c pytorch

Below we give more detailed installation instructions for the different usage scenarios.

Option 1 (GUI, GPU support) (NVIDIA drivers already installed)

Once you have created and activated the conda environment, you can install with GUI support with the following command

pip install idtrackerai[gui]

To get GPU support without having to manually install the CUDA 10.2 or 11.3, you can install PyTorch with GPU support from the Conda package manager with the following command:

conda install pytorch torchvision -c pytorch

This will install the latest version of cudatoolkit. To specify a lower version use the command:

conda install pytorch torchvision cudatoolkit=10.2 -c pytorch

Option 2 (GUI, GPU support) (NVIDIA drivers and CUDA already installed)

If you have already installed CUDA system-wide, then you can install with GUI an GPU support running the command:

pip install idtrackerai[gui,gpu]

This will install the latest version of pytorch and torchvision using PyPI instead of conda.

Option 3 (GUI, no-GPU support)

In some cases, you might not need the GPU support for For example, when tracking single animals, tracking animals without keeping the identities along the video, or when setting the preprocessing parameters to then track the video in a different computer or in a cluster.

In this case, you only need to install with GUI support with the command:

pip install idtrackerai[gui]

Option 4 (no-GUI, GPU support)

You might want to use from the command line and read the pre-processing parameters from a .json file (see instructions to generate a .json file in the Tracking from the terminal section). This can be useful if you have a dedicated computer for tracking multiple videos in batches and you access it with SSH, or if your are going to install in a cluster.

If CUDA is are already installed in your computer system-wide, you only need to run the following command:

pip install idtrackerai[cli, gpu]

If you want Conda to install the CUDA in your Conda environment, then run

pip install idtrackerai[cli]
conda install pytorch torchvision -c pytorch

This will install the latest version of cudatoolkit. To specify a lower version use the command:

conda install pytorch torchvision cudatoolkit=10.2 -c pytorch

Option 4 (no-GUI, no-GPU support)

Some times you might want to install idtrackerai in an environment so that you can manipulate and open files. For that you just need to run the command:

pip install idtrackerai

Note that with this installation mode, you won’t have any CLI or GUI to track videos.

Uninstall and remove the software

As can be now installed using a PyPI, to uninstall it you just need to execute

pip uninstall idtrackerai

If you installed inside of a Conda environment, you can also remove the environment by doing

conda remove -n name-of-the-environment --all