Research on application of model ensemble in sports image classification based on environmental information

Abstract

Abstract In recent years, a deep convolutional neural network has been widely used in the field of image classification. However, training a satisfactory network is very arduous, not only to tune the hyper-parameters in the network but also to avoid the overfitting problem caused by a deep neural network. Another point is that it is difficult for a neural network to learn subtle details without human annotation. Therefore this paper proposed a preferable classification algorithm for sports classification tasks that combines the deep neural network with the object detection algorithm to obtain the prediction result. In this paper, the author compared the differences between classification directly using neural networks and a modified model ensemble approach to classification from a holistic perspective, as well as elaborating the advantages and disadvantages of the two approaches. The conclusion shows that the use of the improved model ensemble classification algorithm performs better than the direct use of neural networks and also achieves a high degree of accuracy in the test set.