  Current state-of-the-art object detection algorithms still suffer the problem
of imbalanced distribution of training data over object classes and background.
Recent work introduced a new loss function called focal loss to mitigate this
problem, but at the cost of an additional hyperparameter. Manually tuning this
hyperparameter for each training task is highly time-consuming.
  With automated focal loss we introduce a new loss function which substitutes
this hyperparameter by a parameter that is automatically adapted during the
training progress and controls the amount of focusing on hard training
examples. We show on the COCO benchmark that this leads to an up to 30% faster
training convergence. We further introduced a focal regression loss which on
the more challenging task of 3D vehicle detection outperforms other loss
functions by up to 1.8 AOS and can be used as a value range independent metric
for regression.
