# EXOLOX: An Efficient One-Stage Object Detection Network Based on YOLOX
## 3 Results and Analysis
Ultimately, we replace the decoupled head with the ERDhead and improve the performance from 40.8% to 41.4% (+0.6% AP). By this point, our proposed method exceeds YOLOX-s by 0.9% AP. Cheap Operation versus Expensive Operation The cheap operation represents the process of interacting with the feature map using the convolution of \(2\times 2\) in our proposed CSPFSA Block, and the expensive operation represents the computation of a small region containing information about the four parts of the feature map still using the Non-local Network. From Table 2, although we can see that the detection results are almost the same for the way we interact with small regions by \(2\times 2\) convolution and for the way to compute self-attention on small regions, the results obtained by the cheap operation are about 13% faster in inference. Additionally, the training time is less than that of the expensive operation under the same conditions. Data Augmentation Strategy The authors in YOLOX mentioned that assigning data augmentation strategies corresponding to the size of the model could help improve performance. Some previous experiments have removed the MixUp [56] and weaken the mosaic (reduce the scale range from [0.1, 2.0] to [0.5, 1.5]). However, when MixUp as well as a larger range of mosaic was used in EYOLOX, the detection performance has been improved substantially, as shown in Table 3. We also found the different performances of the two strategies during the training process, as shown in Figure 7. If EYOLOX uses the augmentation strategy of YOLOX-s, the AP will rise faster during the training process. When the data augmentation is turned off in the last 15 epochs, the AP will come out with a moderate increase, as shown in Figure 7a. After reaching the peak, the phenomenon of overfitting occurs. If the augmentation strategy is increased, the AP rises gently with the training time during the training. When the data augmentation is off in the last 15 epochs, the AP increases dramatically in a very short period of time, and then overfitting also occurs, as shown in Figure 7b. In addition, it can be seen from Figure 7 that no matter which data augmentation strategy is used, the model iteration with 300 epochs is the optimal choice, which is consistent with the method of YOLOX. Clearly, the model gradually tends to be stable or even shows a slight downward trend after 250 epochs. At this time, the model has begun to overfit without closing data augmentation. Subsequently, the accuracy of the model gets higher returns with closing data augmentation. \begin{table}
\begin{tabular}{c c c c c c c c c c} \hline \hline
**Methods** & **AP(\%)** & **AP\({}_{\text{50}}\)** & **AP\({}_{\text{75}}\)** & **AP\({}_{\text{S}}\)** & **AP\({}_{\text{M}}\)** & **AP\({}_{\text{L}}\)** & **Parameters** & **GFLOPs** & **FPS** \\ \hline A & 41.3 & 60.8 & 44.1 & 22.8 & 45.0 & 56.5 & 13.52M & 33.73G & **61** \\ B & 41.3 & 60.9 & 44.3 & 22.7 & 44.8 & 56.6 & 13.46M & 33.76G & 53 \\ A+ESE & 41.4 & 60.9 & 44.2 & 23.0 & 45.3 & 56.5 & 13.54M & 33.74G & **60** \\ B+ESE & 41.5 & 60.9 & 44.5 & 24.2 & 45.3 & 56.8 & 13.48M & 33.77G & 53 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Table of comparison experiments between the two operations. “A” represents the cheap operation, “B” represents the expensive operation and ESE is the channel attention mechanism module. Comparison with Other SOTA Detectors Table 4 shows the comparison of our method with other state-of-the-art detectors on the COCO-2017 validation set. The performance of our detector has been evidently improved on the basis of YOLOX. Except for the performance of small objects is only improved by 0.3% AP, all other evaluation indicators show more than 1.0% AP performance improvement. Specifically, EYOLOX has a significant improvement in its ability to detect large objects, with an APL improvement of 4.3% AP over the YOLOX-s. The results of EYOLOX clearly outperform the PAI-YOLOX [57] proposed by Alibaba team and YOLOv6 proposed by Meituan.