# Human Pose Estimation Based on Lightweight Multi-Scale Coordinate Attention
## 4 Experiments
### Ablation Experiment  
In this paper, the COCO dataset was selected for the ablation experiment. First, the GhostNet, coordinate attention mechanism (CA), and BiFPN modules were separately used on the baseline network to evaluate each component's effect on Yolo-Pose. The size of the input data was fixed at 640 \(\times\) 640 pixels. The pre-training model was not applicable. The initial learning rate was 1 \(\times\) 10\({}^{-2}\). The SGD optimizer was used to update the network parameters. All models were trained from the beginning with the same configuration, and each training iteration was 50 times. Table 5 displays the respective experimental configuration and results. In method (1), as the control group, the network was based on the original Yolo-Pose method, which is constructed by CSP-darknet53-s. Methods (2) to (5) respectively integrated the GhostNet, CA mechanism, and BiFPN modules into the basic Yolo-Pose network. Comparing methods (1) and (2), as we can see, the GhostNet module essentially cut the original network's computation capacity by 5.6 GFLOPs. At the same time, network accuracy mAP increased by 2.3% and a 2.6% increase in AP50 was achieved. These results showed that the GhostNet module can significantly lighten the network while extracting more effective features, thus enhancing the network precision and confirming the practicability of the GhostNet module. Comparing methods (1) and (3), it can be seen that using the coordinate attention mechanism boosted the network precision mAP and AP50 by 1.1% and 0.8%, respectively [29]. These results proved the CA module's capability to accurately extract the position information of the human body, thereby improving the accuracy of the network. Figure 8: FPS vs. mAP of Different Methods on COCO 2017 Validation Dataset. \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Method** & **GhostNet** & **CA** & **BiFPN** & **GMACS** & **[EMAIL_ADDRESS]** & **mAP@.5** \\ \hline (1) & - & - & - & 10.2 & 55.1 & 84.1 \\ (2) & \(\surd\) & - & - & 7.4 & 57.4 & 86.7 \\ (3) & - & \(\surd\) & - & 8.4 & 56.2 & 84.9 \\ (4) & - & - & \(\surd\) & 8.8 & 56.8 & 86.7 \\ (5) & \(\surd\) & \(\surd\) & \(\surd\) & 7.2 & 57.1 & 86.7 \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Ablation Experiment Configuration and Results. \(\surd\) and - denotes “with” and “without” the module respectively. Comparing methods (1) and (4), it can be seen that after replacing the original PANet multi-scale feature fusion module with the BiFPN module, 1.7% and 2.6% improvements were achieved in the original network accuracy mAP and AP50. These results demonstrates that the weighted bidirectional feature pyramid module could obtain and fuse multi-scale feature information in the network more proficiently, improving the accuracy and robustness of the network and validating the effectiveness of our approach. The results of the ablation experiments showed that the GhostNet, coordinate attention mechanism (CA), and bi-directional feature pyramid network (BiFPN) modules contributed significantly to the enhancement of our model's performance. To more accurately assess each enhanced module's contribution to the overall network model, Figure 9 shows the comparison of AP50 of the five methods in the above ablation experiment based on different network computations and on the COCO 2017 validation set. First of all, it is clear from the comparative data in the figure that each enhanced module reduced the computational complexity of the original network to varying degrees, which significantly boosted the accuracy of the network. According to the data of the first 50 iterations, among the GhostNet, coordinate attention mechanism, and BiFPN modules, the GhostNet module contributed the most to the improvement of network lightweight and accuracy. This demonstrated that by breaking down the convolution process and linear transformation operations, GhostNet may considerably improve network accuracy while lowering the computing cost of convolution networks and producing more useful feature maps.