## 4 Experiments
### Object Detection on COCO and VOC
#### 4.2.3 Results and Discussion  
Table 2 compares the model performance and computational cost of various downsampling methods on the object detection task. The model performance of MaxPool is significantly lower than that of the baseline Strided Conv for all accuracy evaluation metrics. Unexpectedly, Softpool, Adapool, and Patch Merging exhibit lower accuracy values in the precision, recall, [EMAIL_ADDRESS], and [EMAIL_ADDRESS]:0.95 metrics. By checking their losses during training, it was found that their loss values for both the training and validation sets gradually become NaN, leading to gradient explosion and a failure to converge. It can be seen that these methods have poor cross-model compatibility and portability and are not suitable for directly replacing Strided Conv in the baseline model. In contrast, the accuracy of SliceSamp was slightly improved on both the COCO and VOC datasets compared to the baseline model. By further replacing the Nearest Neighbor Upsampling layer in the baseline model, we also evaluated the effectiveness of the proposed upsampling method. When all models were trained for 60 epochs on the COCO dataset, SliceUpsamp showed similar performance to the SliceSamp model without replacing the upsampling component. When all models were sufficiently trained for 300 epochs on the VOC dataset, SliceUpsamp outperformed the SliceSamp model by about 0.6% in terms of [EMAIL_ADDRESS]. It can be seen that after sufficient training, our proposed upsampling method can further achieve good performance gains.  
Moreover, it is noteworthy that our proposed method maintains or slightly improves model performance while significantly reducing the computational cost. Compared to the baseline model Strided Conv, SliceSamp reduces the number of parameters by 17.17% and  
\begin{table}
\begin{tabular}{c c c c c c c c c c c} \hline \hline \multirow{2}{*}{**Method**} & \multirow{2}{*}{**Model**} & \multirow{2}{*}{**Params**} & \multirow{2}{*}{**FLOPs**} & \multicolumn{4}{c}{**COCO**} & \multicolumn{4}{c}{**VOC**} \\ \cline{5-10}  & & & & **Precision** & **Recall** & **[EMAIL_ADDRESS]** & **[EMAIL_ADDRESS]** & **Precision** & **Recall** & **[EMAIL_ADDRESS]** & **[EMAIL_ADDRESS]** \\ \hline Strided Conv & YOLOv5s-5.0 & 7.28 M & 17.2 G & 62.18 & 48.60 & 51.91 & 32.05 & 79.71 & 71.16 & 78.50 & 51.75 \\ Maxpool & YOLOv5s-5.0 & 5.23 M & 12.9 G & 58.52 & 44.98 & 67.14 & 27.97 & 76.16 & 70.54 & 74.98 & 47.74 \\ Softpool & YOLOv5s-5.0 & 5.23 M & 12.9 G & 6.02 & 1.11 & 0.57 & 0.32 & 15.17 & 11.75 & 4.95 & 1.98 \\ AdaBoost & YOLOv5s-5.0 & 5.23 M & 12.9 G & 6.04 & 4.02 & 0.36 & 0.09 & 10.80 & 14.11 & 4.41 & 1.68 \\ Patch Merging & YOLOv5s-5.0 & 5.00 M & 14.65 & 0.06 & 0.03 & 0.002 & 0.002 & 0.64 & 8.05 & 0.26 & 0.05 \\ SliceSamp (ours) & YOLOv5s-5.0 & **6.00 M** & **14.7 G** & 6.29 & **48.79** & **52.08** & **32.59** & **88.58** & 71.90 & 78.71 & 53.85 \\
**SliceUpsamp (ours)** & YOLOv5s-5.0 & 6.06 M & 14.8 G & **65.43** & 46.85 & 51.72 & 32.45 & 79.7 & **72.99** & **79.29** & **83.95** \\ \hline \hline \end{tabular}
\end{table}
Table 2: The accuracy and computational cost of the models on the COCO and VOC datasets.  
the number of FLOPs by 14.53%. Experiments show that our method can significantly reduce the number of parameters and computational costs while maintaining the original model performance in a neural network in which Strided Conv is the main downsampling component.