# Diverse Humanoid Robot Pose Estimation from Images Using Only Sparse Datasets
## 5 Results and Evaluation
### Evaluation on Network Architecture  
In this section, we investigate the impact of varying the number of stages in the network architecture on performance.  
To determine the optimal configuration for the benchmark model, we performed evaluations on the DHRP test set using the DSNT regression-based model, with a multi-stage hourglass fully convolutional backbone, as detailed in Section 4.1. Each model was tested with varying numbers of hourglass stages and evaluated using the standard metrics \(AP^{obj}\), \(AR^{obj}\), and \(AR^{int}\), as outlined in Section 4.2. All models were initially pretrained on the COCO dataset and then further trained using the DHRP training dataset.  
\begin{table}
\begin{tabular}{c c c c c c c c c c c c c} \hline \hline
**Configuration** & \(mAP^{obj}\) & **Apo.** & **Atl.** & **Daz.** & **Eve** & **Fig.** & **H1** & **Kep.** & **Opt.** & **Pho.** & **Tal.** & **Tor.** \\ \hline Individual & 51.0 & 48.8 & 57.8 & 61.3 & 38.4 & 27.8 & 49.9 & 90.8 & 50.9 & 34.3 & 42.6 & 58.4 \\ Dataset S & 58.5 & 79.2 & 56.9 & 54.5 & 65.9 & 32.2 & 72.1 & 79.0 & 73.6 & 51.3 & 29.9 & 55.0 \\ Dataset A & 73.9 & 71.4 & 79.9 & 66.9 & 75.3 & 49.4 & 85.1 & 90.6 & 64.4 & 75.0 & 81.6 & 77.5 \\ Dataset D & **84.9** & **85.9** & **88.1** & **79.7** & **82.4** & **69.7** & **95.4** & **94.3** & **80.0** & **83.5** & **91.8** & **87.2** \\ \hline \hline \end{tabular}
\end{table}
Table 8: An ablation study on individual target humanoid robot datasets using the DHRP test set and 4-stage hourglass networks, evaluated with the \(AP^{obj}\) metric. The best results are indicated in bold, while the worst are underlined. The dataset configurations are as follows: Individual = trained separately using only the corresponding target humanoid robot data; Dataset S = arbitrary humanoid robots + synthetic + random background data with no target robot data; Dataset A (Target robots only) and Dataset D (Full) align with the configurations presented in Table 5.  
\begin{table}
\begin{tabular}{c c c c c c c c c c c c c} \hline \hline
**Leave-One-Out** & \(mAR^{obj}\) & **Apo.** & **Atl.** & **Daz.** & **Eve** & **Fig.** & **H1** & **Kep.** & **Opt.** & **Pho.** & **Tal.** & **Tor.** \\ \hline
**Using All** & **75.1** & **74.5** & **80.6** & **73.9** & **72.3** & **63.0** & **89.9** & **90.8** & **67.7** & **62.0** & **81.2** & 77.5 \\ \hline w/o Apo. & 65.6 & 52.5 & 74.8 & 65.9 & 62.4 & 55.3 & 83.7 & 83.2 & 61  
The evaluation results are shown in Tables 10 and 11. Performance consistently improved as the number of stages increased, with a decline observed in the eight-stage hourglass network due to overfitting on the small-scale dataset. These findings indicate that the seven-stage regression-based hourglass network yields the best performance across all metrics. Therefore, for the remaining evaluations on the DHRP dataset, we utilize the seven-stage regression-based hourglass network as the optimal model.  
The network was implemented in PyTorch [67] and trained for 240 epochs using the RMSprop optimizer. Training was conducted on a system with an Intel Xeon Silver 4310 processor, 128 GB RAM, and dual Nvidia RTX 4090 GPUs. The training times for models with different numbers of stages are presented in Table 12.  
The real-time performance of the network models with varying stage numbers was also evaluated, as shown in Table 12. The trained PyTorch models were converted to a universal format using Open Neural Network Exchange (ONNX) [68] and tested in a C++ application. The inference times were measured on a system with an AMD Ryzen 9 5900X 12-Core processor, 64 GB RAM, and an NVIDIA GeForce RTX 3080 Ti GPU.  
The results show that all network models achieved inference speeds exceeding 38 FPS, confirming real-time pose estimation capabilities.