We also tried to utilize these tricks for training CNN-based object detectors. As shown in Table B, these tricks bring 0.2%-0.6% On contrast, this strategy improves the adder detector for 1.2% mAP, which indicates that the It is an interesting topic to explore the robustness to the domain shift for AdderNet-based detector. Figure 1: Qualitative results of RetinaNet [2], FCOS [3] and our proposed Adder FCOS. As shown in Table C, Adder FCOS suffers from 2.2% mAP drop on Cityscapes compared with convolutional counterpart, which is similar with the performance drop on COCO.

The expected reduction in training time when batch size increases is obvious. However, why is there a drastic increase in training time when we increase batch size in Caffe?! Let's have a look at nvidia-smi for both of the Caffe experiments: