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danielkonecny / sports-poses-recognition

Classifying sports poses from image with time-contrastive neural network (self-supervised learning) as a Master's Thesis at BUT FIT.
MIT License
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resnet self-supervised-learning tensorflow2 time-contrastive-network

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Self-Supervised Learning for Sports Pose Recognition

Master's Thesis at Brno University of Technology - Faculty of Information Technology.

Author: Daniel Konecny (xkonec75).

Dependencies

How to install

  1. Clone this repository: git clone https://github.com/danielkonecny/sports-poses-recognition.git.
  2. Enter the repository root directory: cd sports-poses-recognition.
  3. Create a virtual environment: python3 -m venv .env.
  4. Activate the virtual environment: source ".env/bin/activate".
  5. Install the requirements: pip install -r requirements.txt.

How to launch

  1. Enter the project root directory sports-poses-recognition.
  2. Activate the virtual environment: source ".env/bin/activate".
  3. Make sure you have the project root directory in PYTHONPATH because of relative imports in the scripts: export PYTHONPATH="${PYTHONPATH}:.".
  4. Launch all scripts from the root directory, e.g. python3 src/model/Encoder.py ....
  5. Use argument -h or --help for all the necessary information about every script.