Garbage Classification Recognition Wechat Applets Based on ResNet34 Convolutional Neural Network
The productivity level of human society is increasing exponentially,resulting in a skyrocketing amount of garbage,so how to deal with a large amount of garbage has become a tricky problem.At the same time,there are recyclable garbage that can be recycled and harmful garbage that can cause pollution.If it is discarded without distinction,it is a waste of resources.In order to solve the problem of misclassification in the garbage classification process,a garbage classification recognition model based on ResNet34 convolutional neural network is constructed.According to the needs of garbage classification,the existing network model was adjusted accordingly,the main parameters of the model were optimized,and the model was trained by transfer learning,so that the accuracy of the test set reached 87%.Select and combine with Wechat applet to import datasets and train 40 garbage categories to the ResNet34 model,and remotely call the model running on the server through the Https protocol,so as to achieve fast and accurate garbage sorting on the Wechat applet.
NETL
NSTL
万方数据
维普
