The improvements in recent CNN-based object detection works, from R-CNN [11] and Fast/Faster R-CNN [10, 29] to recent Mask R-CNN [14] and RetinaNet [22], mainly come from new network, or framework, or loss design. But mini-batch size, a key factor in the training, has not been well studied. In this paper, we propose a Large Mini-Batch Object Detector (MegDet) to enable the training with much larger mini-batch size than before (e.g. from 16 to 256), so that we can effectively utilize multiple GPUs (up to 128 in our experiments) to significantly shorten the training time. Technically, we suggest a learning rate policy and Cross- GPU Batch Normalization, which together allow us to suc- cessfully train a large mini-batch detector in much less time (e.g., from 33 hours to 4 hours), and achieve even better accuracy. The MegDet is the backbone of our submission (mmAP 52.5%) to COCO 2017 Challenge, where we won the 1st place of Detection task.

本文提出了一种新的大批量目标检测器（MegDet），通过一种学习率政策和交叉GPU批量归一化措施，可以显著缩短培训时间（例如从33小时到4小时）并提高准确性（按标准COCO数据集评估，mmAP为52.5％），从而比以前快训练更大的小批量大小（例如，从16到256个）。

MegDet: 一个大型 Mini-Batch 目标检测器