This may be since the fact that of the aging of giant pandas, their facial features gradually tend to be stable, making it difficult to distinguish the facial features of subadult from adult giant pandas, as well as adult and elderly giant pandas. The model we employed had a better classification performance for the age group of juvenile and adult giant pandas, followed by the subadult group, and the weakest was in classifying the elderly panda age group (Figure 9). The purpose was to reduce the influence of the background and let the model pay more attention to the learning of the foreground. In addition, in order to further improve the accuracy and speed of the network, it is necessary to balance the three dimensions of width, depth, and resolution, EfficientNet (Tan & Le, 2019) was also introduced in this paper as an experimental model for the age group classification of giant pandas.Īs the focus of this study was giant panda facial images, in order to verify the feasibility of the deep learning technology, we first crop the face according to the data annotation (Figure 5). If you haven’t installed any Red Giant products, then the presence of. It’s worth noting that Red Giant Service.exe is not a system file and is only present if you have installed the Red Giant software on your computer. Therefore, in this paper, ResNet (He et al., 2016a) was introduced as one of the experimental models for the age group classification of giant pandas. The Red Giant Service.exe file is responsible for managing and updating the Red Giant plugins installed on your computer. In the case that the network is getting deeper and deeper, the residual connection and identity mapping in the residual block (He et al., 2016b) can effectively avoid gradient disappearance and gradient explosion, and it is easier to optimize and improve the accuracy. Residual learning effectively solved this problem and can train neural networks with more than 1000 layers (He et al., 2016a). Since faces contain many unique attributes and have been previously applied in facial age recognition (Antipov et al., 2017 Levi & Hassncer, 2015 Samek et al., 2017) and individual panda recognition studies (Chen et al., 2020 Hou et al., 2019, 2020).įor giant panda images, a deep neural network needed to be constructed to extract features at a deep level however, the training of deep neural networks faced network degradation problems.
At the same time, for the convenience of defining uniform selection conditions, we chose the face of the giant panda as the focus of the study. In response to the above image collection problems, we screened the dataset.
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