A crtitical part of the task was considered a pipeline organization, once this is established, other model architectures might be explored.
As of now ResNet34 architecture is chosen as a baseline. The standart achitecture is modified with 30 input channels and one output channel. The dataset is written as well to have 30-channel input, first channels of which are filled with chosen images. All parameters are specified in settings.py file.
Create an environment and install dependacies.
conda create -n envname python=3.8
conda activate envname
pip install -r requirements.txt
Modify settings.py file if needed. Once done, you can run training pipeline.
python train.py
Once training is over, results are easily visualized using jupyter notebook 'results_visualization.ipynb'
