Training hyperparameters in this repo are defined in train.py, including augmentation settings:

We began with darknet defaults before evolving the values using the result of our hyp evolution code:

python3 train.py --data data/coco.data --weights '' --img-size 320 --epochs 1 --batch-size 64 -- accumulate 1 --evolve

The process is simple: for each new generation, the prior generation with the highest fitness (out of all previous generations) is selected for mutation. All parameters are mutated simultaneously based on a normal distribution with about 20% 1-sigma:

Fitness is defined as a weighted mAP and F1 combination at the end of epoch 0, under the assumption that better epoch 0 results correlate to better final results, which may or may not be true.

An example snapshot of the results are here. Fitness is on the y axis (higher is better).
from utils.utils import *; plot_evolution_results(hyp)