Weather4cast 2021 IEEE BigData

3rd place solution for the Weather4cast 2021 IEEE BigData Challenge

Dependencies

The code can be executed from a fresh environment using the provided list of requirements: conda env create -f environment.yml.

Inference

A script has been create to made predictions using a trained model on new data as per requirements detailed in competition: (#https://github.com/iarai/weather4cast#code-and-abstract-submission)

The model weights of the final submission for both core and transfer learning can be downloaded from https://drive.google.com/drive/folders/1PXHNNRIcIzb1TywuKf-YU2AEYo_9w2MY?usp=sharing

To run predictions on a test dataset use ('inference.py'). This should fine on a CPU machine

examples of usage:

inference for Region R5 using A42.pth and A44.pth

R=R5
INPUT_PATH=data
WEIGHT_FOLDER=weights
OUT_PATH=.
python weather4cast/inference.py -d $INPUT_PATH -r $R -f $WEIGHT_FOLDER -w -w A42.pth A44.pth -o $OUT_PATH -g 'cuda'

Train/evaluate a UNet

To replicate the training for the 3rd place solutions use of the training notebooks (Training_ModelA.ipynb and Training_ModelB.ipynb).