Classification and Deployment of Animal Images Based on Convolutional Neural Networks and Transferability Estimation

Although the current technology has achieved good results in classifying a large number of animal images, it is more practical to choose the right model and transfer it to the mobile applet application, so that users can quickly identify the classification of animals through the phone. Therefore, to address this problem, this study is divided into three parts: model training and selection; model portability assessment; model optimization and its deployment on the WeChat applet side. First, an optimal model DenseNet121 is selected by comprehensively evaluating the accuracy, parameter counts, precision, F1-Score value and loss value of the model. Then the portability of the model is assessed using the LogME method. Finally, the introduction of the ECA (Efficient Channel Attention) to the selected model resulted in a 0.51 % increase in model accuracy and a 0.46% increase in precision. The breakthrough of applying the large model to the WeChat applet side to realize the fast recognition of animal classes provides a good application case for future research.