# RepNet: A Lightweight Human Pose Regression Network Based on Re-Parameterization
## 4 Experiment  
### Implementation Details  
**Model settings**. RepNet uses several multi-parameterized modules in Figure 4 to stack and implement the feature extraction backbone. Similar to ResNet, in the network, five stages are set up to perform downsampling operations on the feature maps to gradually realize the increase in the number of feature channels. Table 1 demonstrates the specifics of different stages when the input image is 256 \(\times\) 256. In this paper, two models, RepNet-A and RepNet-B, are modelled, using the layer configuration of {1, 2, 4, 14, 1} for the five stages of the network, and different channel scaling factors are set for controlling the number of output channels, respectively. Following the setup for the RepVGG network, the channel scaling factors were set to 0.75 and 2.5 for model A, and 1 and 2.5 for model B. Model B was used to control the number of output channels. For the RLE module, the fixed distribution terms in Equation (13) are replaced with the Laplace transform as setup in the original paper. In the training, we use the off-the-shelf flow generation model RealNVP [35] for learning the residual likelihood estimation, and the reversible transformation function is obtained by multiple stacked fully connected layers, where the number of fully connected layers is set to 3, with each layer featuring 64 neutrons. The dimension of the fully connected layers of the final output result is set to \(K\times 4\), and \(K\) is the number of predicted keypoints. In the testing phase, the average of the network predictions \(\hat{\mu}\) will be used as the result of coordinate regression. **Dataset**. We verified the effectiveness of the method on COCO [7] and MPII [8] datasets, respectively. The COCO dataset annotated 17 systemic keypoints for 250 K \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Network Phase** & **Output Size** & \
\begin{tabular}{c} **Channel Numbers of** \\ **RepNet-A** \\ \end{tabular} &
\begin{tabular}{c} **Channel Numbers of** \\ **RepNet-B** \\ \end{tabular} \\ \hline
1 & \(128\times 128\) & \(64\times 0.75\) & 64 \\ \hline
2 & \(64\times 64\) & \(64\times 0.75\) & 64 \\ \hline
3 & \(32\times 32\) & \(128\times 0.75\) & 128 \\ \hline
4 & \(16\times 16\) & \(256\times 0.75\) & 256 \\ \hline
5 & \(8\times 8\) & \(512\times 2.5\) & \(512\times 2.5\) \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Backbone configuration. individuals in more than 200 K images. Except for 57 K images that were used for training, it also included a validation set of 5 K images and a test set of 20 K for evaluating training results. The MPII dataset contains approximately 25 K images with over 40,000 individual annotations, highlighting a total of 16 keypoints in the human body. We divided the data into a 22 K training set and a 3 K test set, according to the official criteria. **Evaluation indicators**. Average precision (AP) based on object keypoint similarity (Oks) is used as the evaluation index in COCO, and its calculation equivalent is shown in Equation (14). MPII calculates the average PCK index under the given threshold to evaluate the performance of the algorithm, as is shown in (15):  
\[AP_{M}=\frac{\sum_{m}\sum_{p}\delta\big{(}oks_{p}>T\big{)}}{\sum_{m}\sum_{p}1} \tag{14}\]  
\[PCK^{k}_{mean}=\frac{\sum_{p}\sum_{i}\delta\bigg{(}\frac{d_{gi}}{d^{p}_{p}} \leq T_{k}\bigg{)}}{\sum_{p}\sum_{i}1} \tag{15}\]  
**Train settings**. All workflows of the RepNet network are based on the MMPose framework and are validated on the COCO and MPII datasets, respectively. All training images are pre-processed with random rotation of \(\pm\)30 degrees and random scale scaling of \([0.75,1.25]\). During the training phase, the COCO dataset is processed by the network using an input size of 256 \(\times\) 192. The learning rate used for the COCO dataset is 1 \(\times\) 10\({}^{-3}\), and the learning rate trained for the MPII dataset is 5 \(\times\) 10\({}^{-4}\).