  Although two-stage object detectors have continuously advanced the
state-of-the-art performance in recent years, the training process itself is
far from crystal. In this work, we first point out the inconsistency problem
between the fixed network settings and the dynamic training procedure, which
greatly affects the performance. For example, the fixed label assignment
strategy and regression loss function cannot fit the distribution change of
proposals and thus are harmful to training high quality detectors.
Consequently, we propose Dynamic R-CNN to adjust the label assignment criteria
(IoU threshold) and the shape of regression loss function (parameters of
SmoothL1 Loss) automatically based on the statistics of proposals during
training. This dynamic design makes better use of the training samples and
pushes the detector to fit more high quality samples. Specifically, our method
improves upon ResNet-50-FPN baseline with 1.9% AP and 5.5% AP$_{90}$ on the MS
COCO dataset with no extra overhead. Codes and models are available at
https://github.com/hkzhang95/DynamicRCNN.
