1. Be sure you don't have any 'active' images or containers:

1.1) docker rm -f -v $(docker ps -aq)

1.2) docker rmi -f $(docker images -q)

1.3) docker volume rm $(docker volume ls -q)

2Change the parameters in the file conf.yaml, in case you want, except the outputs parameter

2. Run it: python main.py -c conf/conf.yaml -r [state] -i [image] --preproc [preprocessing method] For example: python main.py -c conf/conf.yaml -r full -i in/SF14/day1_low10.bmp For 'state' parameter you can choose:

• 'preproc': it only does image preprocessing
• 'prepare': it does Preprocessing -> Train -> Test -> Performance plots and metrics
• 'classify': it classifies the respective image. You must have a trained model first and point to it with the -m flag
• 'full': it does everything from preprocessing to classification

Obs: The results from training and testing are saved in /out/outputs.txt

In conf.yaml we can choose the parameters:

• optim: 'Adam'
• model: {'densenet', 'resnet', 'efficientnet'}
• epochs: Any integer greater than 0
• batch: 16
• lr: !!float 5e-4
• raw: 'in/'
• preproc: {'skip', 'remove_background', 'gaussian', 'median', 'bilateral', 'unsharp'}