# Single-Shot Object Detection with Split and Combine Blocks
## 4 Experiments
### Ablation Study  
We adopted a variety of recent training tricks to improve the performance of SCNet. The ablation study begins with a baseline network which uses Batch Normalization layer [50], Relu activation, and decays the learning rate every 30 epochs. The baseline network is trained with the fixed input resolution of 608 \(\times\) 608 and directly predicts the size of the bounding box with the exponential function. Herein, we evaluate the impetus of these elements, including CmBN, Mish, Admix, cosine, and warmup learning rate schedule, multi-scale training, DropBlock and SPP, to the final detection performance. SC-59 is chosen as the backbone network. The results can be found in Table 1, which are measured at 608 \(\times\) 608 input resolution. Shorten training cycle time and save computation costs by transfer learning. All of the mentioned design decisions generate decent accuracy growth, and our detector achieves an AP of 38.8% on COCO val split after using these techniques in the training process. CmBN enhances the quality of statistics in the case of small batch sizes. Admix accumulates information from five different images, and, thanks to them, we complete the training of detectors on two GPUs. The learning rate scheduler, data augmentation, multi-scale training strategy, and Mish activation do not any add additional parameters, and there is almost no increase in inference time after usage. At each level, GA increases only two convolution layers; SPP introduces three max pooling layers and one concatenation operation, and the improvements are minimal in cost. DropBlock quits work during inference, and no additional computation is added. Our detector reaches 40 FPS on RTX 2080 ti GPU. The backbone SC-59 is then replaced with SC-47 and SC-32 networks to achieve different trade-offs in speed-accuracy. There is a similar structure to these backbone networks, with Figure 3 for reference. We employ SC-[2,1,2], SC-[3,1,1,2], SC-[3,2,2,4], SC-[4,2,2,4,6], SC-[3,2,2,4] blocks and residual blocks to construct SC-47 network, and use SC-[2,1,2], SC-[3,1,1,2], SC-[3,2,2,4], SC-[3,1,1,2], SC-[2,1,2] blocks and residual blocks to construct the SC-32 network. The evaluation results on the COCO val split are illustrated in Table 2. SCNet is tested on the same machine and uses all verified tricks in the training process. SCNet achieves a good speed-accuracy trade-off on COCO val split, with 27.6% AP at 67.0 FPS and 38.8% AP at 40 FPS. \begin{table}
\begin{tabular}{l c c c c c c c c} \hline \hline  & **Baseline** & & & & & & **SCNet** \\ \hline CmBN & \(\surd\) & \(\surd\) & \(\surd\) & \(\surd\) & \(\surd\) & \(\surd\) & \(\surd\) & \(\surd\) \\ Mish & & \(\surd\) & \(\surd\) & \(\surd\) & \(\surd\) & \(\surd\) & \(\surd\) & \(\surd\) \\ Admix & & & \(\surd\) & \(\surd\) & \(\surd\) & \(\surd\) & \(\surd\) & \(\surd\) \\ warmup+cosine & & & & \(\surd\) & \(\surd\) & \(\surd\) & \(\surd\) & \(\surd\) \\ multi-scale training & & & & & & \(\surd\) & \(\surd\) & \(\surd\) \\ GA & & & & & & & \(\surd\) & \(\surd\) \\ DropBlock+SPP & & & & & & & & \(\surd\) \\ \hline COCO val AP & 32.1 & 33.3 & 34.1 & 35.2 & 35.9 & 36.8 & 37.7 & 38.8 \\ FPS & 46 & 46 & 46 & 46 & 46 & 46 & 44 & 40 \\ \hline \hline \end{tabular}
\end{table} 
Table 1: The effect of each trick on the baseline network. All of the listed design decisions generate presentable performance gains in AP—evaluated on the COCO val split.