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A multi-task learning model built for image segmentation made as a group project for the Deep Learning course at UCL

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Multi-Task Learning

How to run main code:

  • Clone the repository.
  • Create a new environment from requirements.txt.
  • Run main.py.

How to use this as a library:

  • You can import functions from from the different files. The code is designed to behave like a module.
  • In particular, the function get_prebuilt_model is very useful if you want to load in-built (default) models.
  • You can add new defaults based on configurations that you build.
  • It is also possible to build models in a bespoke way.

How to run legacy code:

  • The legacy code behaves slightly differently to the main code. It is included for reference.
  • In particular, main_colab.py and colab_continue_train.py are useful if you are running on Colab and have trouble with runtimes restarting. You can use them to save models and then re-train from the previously saved state.
  • main.py from legacy should not be used.

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A multi-task learning model built for image segmentation made as a group project for the Deep Learning course at UCL

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  • Python 62.2%
  • Jupyter Notebook 37.8%