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A multi-animal tracking algorithm based on convolutional neural networks

Project description (v4) is a multi-animal tracking software for laboratory conditions. This work has been published in Nature Methods [1] (pdf here)

What is new in idtrackerai v4?

  • Works with Python 3.7.
  • Remove Kivy submodules and stop support for old Kivy GUI.
  • Neural network training is done with Pytorch 1.10.0.
  • Identification images are saved as uint 8.
  • Crossing detector images are the same as the identification images. This saves computing time and makes the process of generating the images faster.
  • Improve data pipeline for the crossing detector.
  • Parallel saving and loading of identification images (only for Linux)
  • Simplify code for connecting blobs from frame to frame.
  • Remove unnecessary execution of the blobs connection algorithm.
  • Background subtraction considers the ROI
  • Allows to save trajectories as csv with the advanced parameter CONVERT_TRAJECTORIES_DICT_TO_CSV_AND_JSON (using the file).
  • Allows to change the output width (and height) of the individual-centered videos with the advanced parameter INDIVIDUAL_VIDEO_WIDTH_HEIGHT (using the file).
  • Horizontal layout for graphical user interface (GUI). This layout can be deactivated using the setting NEW_GUI_LAYOUT=False.
  • Width and height of GUI can be changed using the using the GUI_MINIMUM_HEIGHT and GUI_MINIMUM_WIDTH variables.
  • Add ground truth button to validation GUI.
  • Added "Add setup points" featrue to store landmark points in the video frame that will be stored in the trajectories.npy and trajectories_wo_gaps.npy in the key setup_poitns. Users can use this points to perform behavioural analysis that requires landmarks of the experimental setup.
  • Improved code formatting using the black formatter.
  • Better factorization of the TrackerApi.
  • Some bugs fixed.
  • Better documentation of main objects (video, blob, list_of_blobs, fragment, list_of_fragments, global_fragment and list_of_global_fragments)
  • Dropped support for MacOS

Hardware requirements (v4) has been tested in computers with the following specifications:

  • Operating system: 64bit GNU/linux Mint 19.1 and Ubuntu 18.4
  • 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 or GeForce GTX 1080 Ti
  • RAM: 32Gb-128Gb (depending on the needs of the video).
  • Disk: 1TB SSD is coded in python 3.7 and uses Pytorch libraries (version 1.10.0). 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.


Frist of all, make sure that you have the latest version of the CUDA driver installed (currenly tested with 495.44)

The recomended way to install v4 is using the following commands:

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

This will install the latest version of pytorch (1.10.0) and torchvision (0.11.1) and the cudatoolkit (version 11.3.1).

NOTE: You can install a lower version of the cudatoolkit using the command conda isntall pytorch torchvision cudatoolkit=10.2 -c pytorch

NOTE: If your computer does not have support for GPU computing, then install pytorch with the cpuonly mode activated. So, you just need to change the last line by: conda install pytorch torchvision cpuonly -c pytorch

NOTE: Check a more complete version of the installation instructions in the documentation.

Test the installation.

Once is installed, you can test the installation running one of the following options.

1.GPU support: If you installed it using any of the GPU support options , then run:


2.No GPU support: If you installed it using the no GPU option, then run:

idtrackerai_test --no_identities

Installation for developers.

1.- Clone the repository. In Windows, run this step in the Git Shell:

git clone idtrackerai_dev

2.- Initialize all the submodules. In Windows, run this step in the Git Shell:

cd idtrackerai_dev 
git checkout v4-dev
git submodule update --init --recursive

3.- Create a conda environment using the dev-environment.yml file and activate it. In Windows, run the following steps in the Anaconda Prompt terminal:

conda env create -f dev-environment.yml python=3.7
conda activate idtrackerai_dev 

4.- Execute the file:


Open or run

To run just execute the following command inside of the corresponding conda environment:


If you want to execute using the terminal_mode and loading a .json file where the parameters are stored using the following command:

idtrackerai terminal_mode --load your-parameters-file.json --exec track_video

Go to the Quick start
and follow the instructions to track a simple example video and learn to save the preprocessing parameters to a .json file.

Notes for delevopers

This repository contains's algorithm, the repository (submodule) idtrackerai-app contains the CLI and GUI to track videos using the's algorithm.

The validation GUI used to check the results of the tracking is integrated inside of a bigger project called Python-Video-Annotator. The's validation GUI is a plugin inside of this bigger project, but it has its own repository, the pythonvideoannotator-module-idtrackerai].

We coded's GUI in this way so that in the future other CLI or GUI can be coded without affecting the algorithm, or the algorithm can be modified without affecting the current GUI and CLI.

Documentation and examples of tracked videos

Check more information in the webpage


  • Francisco Romero-Ferrero (2015-)
  • Mattia G. Bergomi (2015-2018)
  • Ricardo Ribeiro (2018-2020)
  • Francisco J.H. Heras (2015-)
  • Antonio Ortega (2021-)
  • Jordi Torrents (2022-)


This file is part of a multiple animals tracking system described in [1]. Copyright (C) 2017- Francisco Romero Ferrero, Mattia G. Bergomi, Francisco J.H. Heras, Robert Hinz, Gonzalo G. de Polavieja and the Champalimaud Foundation. is free software (both as in freedom and as in free beer): you can redistribute it and/or modify it under the terms of the GNU General Public License as published by the Free Software Foundation, either version 3 of the License, or (at your option) any later version. In addition, the authors chose to distribute it free of charge by making it publicly available (

This program is distributed in the hope that it will be useful, but WITHOUT ANY WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU General Public License for more details. In addition, we require derivatives or applications to acknowledge the authors by citing [1].

You should have received a copy of the GNU General Public License along with this program. If not, see

For more information please send an email ( or use the tools available at

[1] Romero-Ferrero, F., Bergomi, M.G., Hinz, R.C., Heras, F.J.H., de Polavieja, G.G., Nature Methods, 2019. tracking all individuals in small or large collectives of unmarked animals. (F.R.-F. and M.G.B. contributed equally to this work. Correspondence should be addressed to G.G.d.P:

F.R.-F. and M.G.B. contributed equally to this work. Correspondence should be addressed to G.G.d.P:

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