# Two-Dimensional Ultra Light-Weight Infant Pose Estimation with Single Branch Network
## 4 Discussion  
The findings from the research and experimental endeavors provide valuable insights into strategies for diminishing the size and intricacy of CNN models. Experiments are meticulously designed to compare the performance of blocks and layers both before and after reduction and replication. This comparative analysis enables us to discern performance variations and evaluate the effectiveness of each block, thereby facilitating informed decisions in model design. As mentioned, the first 12 layers of MobileNet-v1 are used for the backbone, and Table 2 compares the average precision between Openpose models instead of backbones and  
Figure 8: Visualization analysis of pose estimation with the proposed method on the COCO dataset. Figure 7: (**a**) Original image. (**b**) Visualization analysis of confidence maps for joint detection. (**c**) Visualization of an example of a part affinity field used for linked body parts. (**d**) Visualization analysis of the pose estimation with the proposed method on the infant test set. shows that by using the first 13 layers, MobileNet v1 has the advantage of representations of best AP but higher operations costs; therefore, we accept the option of the first 12 layers of MobileNet v1 to achieve a balance between performance and accuracy. Notably, there exists a substantial contrast in GFLOPs between the initial 12 layers and the subsequent layers in the MobileNet-v1 model. In response, we have intervened to fine-tune the model by using a dilated layer, aiming to capture more expansive features effectively. Dilated layers are helpful in expanding the perception area, broadening the neural network's field of view, thereby enhancing its capacity to observe a wider range of input data. This augmentation facilitates improved recognition of larger objects and offers supplementary contextual information pertaining to the surroundings of the object in focus [32]. In the experiment evaluating the blocks of the Openpose-2016 model, we systematically recreated them to precisely assess the performance of each block. Our findings revealed that stages 2-6 exhibited uniformity with negligible changes in accuracy; these stages shared identical sizes and were computationally intensive. Notably, [13] acknowledged this issue and opted to rebalance the number of stages for confidence maps (CMs) and part affinity fields (PAFs). Their results demonstrated that stages 1 and 2 were particularly effective in yielding satisfactory outcomes. Hence, in our study, we focused on enhancement by exclusively utilizing these two stages. In Openpose-2018, the number of stages remained at 6, with each stage utilized for inference regarding specific segments of the CMs or PAFs results. In our investigation, we endeavored to utilize two stages, with each stage employed for inference for both CMs and PAFs. This approach ensures that the results undergo refinement across two stages, as opposed to just one, thereby potentially enhancing the overall accuracy and quality of the outputs when reducing the complexity of the model. Training models using adult datasets have proven advantageous due to the substantial knowledge transfer from a larger pool of relevant data [17]. Transfer learning serves as a crucial mechanism for refining the model's weights to facilitate adaptation with newborns' smaller joint ratios. This research still has several limitations. While we have gathered and contributed data ourselves, the variability in data within consecutive frames of the video remains limited, posing a significant challenge in capturing the diversity of the infants' data for training. Additionally, poses with high complexity are occasionally misinterpreted, a challenge that could potentially be mitigated by augmenting the dataset with more infant-specific data. Moreover, the model's performance tends to degrade in instances where the infant's spine axis is not aligned vertically (rotational angle in the data).