  Compared with model architectures, the training process, which is also
crucial to the success of detectors, has received relatively less attention in
object detection. In this work, we carefully revisit the standard training
practice of detectors, and find that the detection performance is often limited
by the imbalance during the training process, which generally consists in three
levels - sample level, feature level, and objective level. To mitigate the
adverse effects caused thereby, we propose Libra R-CNN, a simple but effective
framework towards balanced learning for object detection. It integrates three
novel components: IoU-balanced sampling, balanced feature pyramid, and balanced
L1 loss, respectively for reducing the imbalance at sample, feature, and
objective level. Benefitted from the overall balanced design, Libra R-CNN
significantly improves the detection performance. Without bells and whistles,
it achieves 2.5 points and 2.0 points higher Average Precision (AP) than FPN
Faster R-CNN and RetinaNet respectively on MSCOCO.
