# EXOLOX: An Efficient One-Stage Object Detection Network Based on YOLOX
## 3 Results and Analysis  
All experiments are trained on COCO-2017 training set. For the comparison of ablation experiments and other existing methods, the COCO-2017 validation set will be used for evaluation. Implementation Details Extensive experiments are conducted in the same environment. The model is trained for a total of 300 epochs with 5 epochs warmup, and stochastic gradient descent (SGD) is used for training. The learning rate is set as \(lr\)\(\times\) batch size / 64, with an initial \(lr\) = 0.01 and the cosine \(lr\) schedule. The weight decay is 0.0005, and the SGD momentum is 0.9. The input-size is evenly drawn from 448 to 832 with 32 strides. Furthermore, the model is trained with a batch size of 64 on a single NIVDIA A40, and the training program is almost identical to YOLOX, except for the use of device and the setting of the batch size. Ablation Experiment The YOLOX with PANet removed is used as a baseline and the final result of EYOLOX is compared with YOLOX-s. As shown in Table 1, the performance is reduced from 40.5% to 33.1% (\(-\)7.4% AP) after PANet removal. When the CSPD Block is added, the performance is improved from 33.1% to 37.0%(+3.9% AP). In particular, the ability of detecting large objects is 3.4% higher than YOLOX-s. When the CSPFSA Block is employed, the performance of the detector increases from 37.0% to 38.6% (+1.6% AP). However, the computation of self-attention requires a dot product of the matrix which results in a 15% drop in FPS. \begin{table}
\begin{tabular}{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 YOLOX-s [16] & 40.5 & 59.3 & 43.7 & 23.2 & 44.8 & 54.1 & 9M & 26.8G & 80 \\ -PANet & 33.1(\(-\)7.4) & 50.2 & 36.1 & 13.5 & 35.9 & 44.1 & 6.1M & 21.3G & 95 \\ +CSPD & 37.0(+3.9) & 55.0 & 39.3 & 16.2 & 39.7 & 57.5 & 8.3M & 23.5G & 88 \\ +CSPFSA & 38.6(+1.6) & 55.9 & 41.3 & 17.3 & 41.6 & 58.3 & 18.4M & 37.5G & 68 \\ +CSPSPPF & 39.3(+0.7) & 56.6 & 42.2 & 18.9 & 42.5 & 58.3 & 18.3M & 37.4G & 67 \\ +SimASFF & 40.8(+1.5) & 60.3 & 43.8 & 22.5 & 44.2 & 55.9 & 15.1M & 35.2G & 55 \\ +ERDFHead & **41.4**(+0.6) & **60.9** & **44.2** & **23.0** & **45.2** & **56.5** & **13.5M** & **33.7G** & **60** \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Ablation experiments of EYOLOX in terms of AP (%) on COCO-2017 val. All the models are tested at 640 \(\times\) 640 resolution, with FP16-precision and batch = 1 on a NVIDIA A40. The FPS in this table are post-processed. Figure 6: An illustration of the structure of ERDHead. In the figure, we add the features in the encoder in the form of residual edges to the features used for classification and regression after the \(3\times 3\) convolution operation. From Table 1, we can clearly find that the amount of parameters of the model becomes larger after adding the CSPFSA Block because of the adjustment of the detection head channel dimension and the increased number of parameters. When CSPSPPF is appended, the detection performance improves from 38.6% to 39.3% (+0.7% AP) without increasing the number of parameters and GFIOPs. Here we found that although the detection performance for detecting large objects is already high, the detection performance of small objects is lower than YOLOX-s by 4.3% AP. Fortunately, this problem is solved when the SimASFF module is introduced, which increases the performance from 39.3% to 40.8% (+1.5% AP) and increases the APs from 18.9% to 22.5% (+3.6% AP). However, the accuracy of AP\({}_{\text{L}}\) descents by 2.4%, which may be caused by incomplete filtering of contradictory information and lead to the uneven distribution of samples and affect the calculation of loss. However, compared with the contribution of SimASFF to the overall performance, the slight impact on the detection of large objects is acceptable.