# HP-YOLO: A Lightweight Real-Time Human Pose Estimation Method
## 4 Experiment
### HP-YOLO Ablation Study
#### 4.5.2 Pruning Experiment  
To assess the impact of our pruning strategy, Figures 7 and 8 illustrate the changes in channel count and inference speed, respectively. Figure 7 displays a stacked area chart depicting the number of channels before and after pruning, where the orange area represents the pruned channels and the gray area indicates the retained channels. The significant reductions observed across most layers underscore the effective removal of redundant channels, thereby reducing model complexity and improving computational efficiency. To determine an appropriate pruning level, we conducted experiments with varying pruning intensities, denoted as 'L1-OKS X.X' in Figure 8. Here, 'L1-OKS 1.0X' refers to pruning based on a base L1-norm threshold \(\tau\). Subsequent configurations, 'L1-OKS 1.5X', 'L1-OKS 1.75X', and 'L1-OKS 2.0X', represent pruning with thresholds scaled by factors of 1.5, 1.75, and 2.0, respectively, i.e., 1.5\(\tau\), 1.75\(\tau\), and 2.0\(\tau\). The base threshold \(\tau\) was empirically determined by starting with a small value and iteratively increasing it while monitoring the validation set performance, selecting a value that initiated effective pruning  
Figure 6: Effective Receptive Field heatmaps comparing the baseline model (**a**) and HP-YOLO model (**b**).  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Method** & **t = 20\%** & **t = 30\%** & **t = 50\%** & **t = 99\%** \\ \hline YOLOv8n & 3.45 & 5.7 & 12.5 & 76.2 \\ Ours & 4.31 & 7.3 & 15.4 & 92.3 \\ \hline \hline \end{tabular}
\end{table}
Table 5: Effective Receptive Field (ERF) quantitative comparison at different thresholds.  
without significant accuracy loss. Figure 8 compares the model's performance metrics before and after pruning, with mean Average Precision (mAP@50) displayed on the primary y-axis (in red) and inference latency on the secondary y-axis (in blue). Observing Figure 8, the 'L1-OKS 1.5X' configuration achieves a 1.4\(\times\) improvement in inference speed, with only a 1% reduction in mAP@50, demonstrating that the pruning strategy effectively accelerates inference while maintaining competitive accuracy. While 'L1-OKS 1.75X' and 'L1-OKS 2.0X' offer further speed improvements, they also lead to a more pronounced drop in mAP@50. Therefore, we selected the 'L1-OKS 1.5X' configuration as our final pruned model, as it provides an optimal balance between inference speed enhancement and accuracy preservation for real-time bedside HPE applications. This configuration effectively reduces model complexity while maintaining competitive performance, making it well suited for deployment in resource-constrained environments, such as mobile devices. In summary, our approach significantly reduces computational complexity and enhances inference speed, making it well suited for deployment in resource-constrained environments.  
Figure 8: Model performance before and after pruning. Pruning improves inference speed (blue) with minimal impact on mAP@50 (red).  
Figure 7: Channel count before and after pruning. The orange area represents pruned channels, and the gray area indicates remaining channels.