## 6 Experimental Results
### Comparisons  
In this section, we conduct comparisons with state-of-the-art methods in the computer vision field: Fast R-CNN [3], YOLOv2 [35], YOLOv2 tiny [36], YOLOv4 [25], YOLOv4 [36], YOLOv5 [37] and MobileNet [4]. Fast R-CNN was the first neural network used to study ship detection [10]. YOLO series are often taken as a baseline to construct lightweight ship detectors [5; 6; 21; 22], and MobileNet is often adopted as a lightweight backbone [18; 19]. \begin{table}
\begin{tabular}{l c c c c} \hline \hline
**Architectures** & **Batch Size** & **Net** & **Post\({}_{mt}\)** & **All\({}_{mt}\)** \\ \hline \multirow{4}{*}{H\_Arch\_32} & 500 & n/a & n/a & n/a \\  & 128 & 279 & 3955 & 261 \\  & 16 & 277 & 3918 & 259 \\  & 8 & 274 & 3863 & 256 \\  & 1 & 180 & 3693 & 172 \\ \hline \multirow{4}{*}{H\_Arch\_4} & 500 & 1886 & 3857 & 1267 \\  & 128 & 1855 & 3764 & 1243 \\ \cline{1-1}  & 16 & 1747 & 3870 & 1204 \\ \cline{1-1}  & 8 & 1584 & 3594 & 1099 \\ \cline{1-1}  & 1 & 202 & 3638 & 192 \\ \hline \multirow{4}{*}{S\_Arch\_C\_32} & 500 & n/a & n/a & n/a \\  & 128 & 267 & 3992 & 249 \\ \cline{1-1}  & 16 & 265 & 3966 & 248 \\ \cline{1-1}  & 8 & 262 & 3935 & 246 \\ \cline{1-1}  & 1 & 172 & 3655 & 164 \\ \hline \multirow{4}{*}{S\_Arch\_C\_4} & 500 & n/a & n/a & n/a \\  & 128 & 1769 & 3994 & 1226 \\ \cline{1-1}  & 16 & 1667 & 3613 & 1141 \\ \cline{1-1}  & 8 & 1561 & 3653 & 1085 \\ \cline{1-1}  & 1 & 186 & 3759 & 177 \\ \hline \hline \end{tabular}
\end{table} 
Table 9: FPS with different batchsizes. In the comparisons, we also use the detection head and the cross-entropy loss function of Darts_tiny to construct a MobileNet-based ship detector. In addition, we keep the default parameters for each neural network and do not use special tricks to improve the performance on purpose. In Figures 11 and 12, we depict two scatter diagrams to generally show our results and the comparison baselines. In both scatter diagrams, the horizontal axis represents F1 score obtained on HRSID. In Figure 11, the vertical axis expresses the detection speed in terms of FPS (\(log_{2}\)). Note that to clearly show each point, we use a logarithmic value rather than the original FPS. Our results are mainly distributed over the top right corner, demonstrating that the neural networks generated by Darts_Tiny are competitive in both accuracy and efficiency. In Figure 12, the horizontal axis represents the model volume (\(log_{2}\)). Similarly, we depict logarithmic values in the diagram. Our results locate over the bottom right corner, implying that the neural networks generated are light and not bad in detection accuracy. Detailed comparison results are given in Table 10. The amount of parameters, model volume, the amount of computations (represented by FLOPs, 800 \(\times\) 800 images), epochs of training, time/epoch (many factors, even including coding quality, can affect the absolute training time. For the sake of fairness, we use the training epochs required for convergence and the average time costs per epoch together to represent the training costs. One epoch here is a round that all images in the training set are put into the model for one time. ), AP, F1 and FPS are listed. We selected two architectures, H_Arch_32 and H_Arch_C_8, to join the comparison, as the former has the best performance in detection accuracy and the latter demonstrates a good balance in the two criteria. Generally, H_Arch_32 and H_Arch_C_8 achieve a significant advantage in the amount of parameters, model volume and FLOPs. Clearly, H_Arch_C_8 achieves a prominent detection speed in terms of FPS. Furthermore, the two neural networks do not show a distinct weakness in training costs (Fast R-CNN consumes 20 epochs to converge. However, it costs more in each epoch, and on the other  
Figure 11: Scatter diagram: FPS vs. F1. Figure 12: Scatter diagram: model volume vs. F1. hand, the low detection accuracy implies that additional training epochs are useless. ), and they are still competitive in detection accuracy.