# EXOLOX: An Efficient One-Stage Object Detection Network Based on YOLOX
## 3 Results and Analysis
To be specific, the performance of EYOLOX is 0.8% AP higher than that of PAI-YOLOX, which obviously has a greater advantage. Furthermore, the performance of EYOLOX is 1.1% AP higher than that of YOLOv6-T, and 1.9% AP higher than that of YOLOv6-T with trained for the same epoch. Compared with these SOTA detectors, EYOLOX uses a smaller number of parameters and GFLOPs. In addition, it outperforms some larger models. For example, the size of EYOLOX model is three times smaller than others. It is still higher than DETR and YOLOF by 0.2% AP and 0.6% AP, respectively. However, DETR with transformer architecture is 2.7% higher on AP\({}_{\text{L}}\) compared with EYOLOX due to the absolute advantage of transformer in extracting features. It is evident from Table 4 that the detector using only single-layer features for prediction is weaker on AP\({}_{\text{S}}\), which also proves the importance of multi-scale prediction. Since single-layer features lack detailed information for detecting small objects, using multiple-scales predictions can make up for the loss of details. However, shallow semantic information still needs to be compensated. FPN used in YOLOv5-SPD-s can effectively solve this problem. Although YOLOv5-SPD-s in the table increases the AP\({}_{\text{S}}\) of YOLOv5-s by 2.3% by avoiding the information loss caused by feature size reduction, this method is relatively expensive. In comparison, EYOLOX has the same AP\({}_{\text{S}}\) as YOLOv5-SPD-s [58], mainly benefiting from the self-attention mechanism to obtain richer semantic information, while the SimASFF module also filters out contradictory information through weighted operations. \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Methods** & **Scale Jit.** & **Extra Aug** & **AP (\%)** \\ \hline EYOLOX & [0.5–1.5] & - & 41.4 \\ EYOLOX & [0.1–2.0] & MixUp & 42.2 (+0.8) \\ EYOLOX \({}^{+}\) & [0.5–1.5] & - & 41.5 \\ EYOLOX \({}^{+}\) & [0.1–2.0] & MixUp & 42.2 (+0.7) \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Comparison of different augmentation strategies in EYOLOX. “Scale Jit.” stands for the range of scale jittering for mosaic image. The ”+” stands for the expensive operation mentioned above. Figure 7: Comparison of training AP changes for two data enhancement strategies applied to EYOLOX. (**a**) does not use MixUp and reduces the mosaic dithering range. (**b**) uses MixUp and increases the mosaic dithering range. Efficiency Evaluation To verify the efficiency of the proposed method in solving image problems on other datasets, we also conducted comparative experiments on the PASCAL VOC 07+12 dataset, as shown in Table 5. Since some models in recent years only released the experimental results on the COCO 2017 dataset, the evaluation results in Table 5 are obtained in our experimental environment. From Table 5, we can find that our method can still produce better results. In addition, we chose 165 epochs as the final iteration in the table after 300 epochs of iteration for the three models is performed, as shown in Figure 8. It can be seen that 300 epochs of iteration are inefficient for this dataset, and the peak before data augmentation has been reached at 150 epochs. Visual Comparison We demonstrate the differences in the results when the same image was processed by EYOLOX and two other detectors, as shown in Figure 9. Figures 9a and 9c are the detection results of EYOLOX, Figure 9b is the detection result of YOLOX-s and Figure 9d is the detection result of YOLOv5-s. From these results, it can be seen that the confidence predicted by EYOLOX is higher and can detect small objects.