  Training heuristics greatly improve various image classification model
accuracies~\cite{he2018bag}. Object detection models, however, have more
complex neural network structures and optimization targets. The training
strategies and pipelines dramatically vary among different models. In this
works, we explore training tweaks that apply to various models including Faster
R-CNN and YOLOv3. These tweaks do not change the model architectures,
therefore, the inference costs remain the same. Our empirical results
demonstrate that, however, these freebies can improve up to 5% absolute
precision compared to state-of-the-art baselines.
