Imbalanced Thangka Image Classification research Based on the ResNet Network

Abstract

Abstract Aiming at the problem of performance degradation caused by ignoring the softmax score of the wrong class in the training process of the unbalanced data set of Thangka images and the problem of the loss of negative feature information in the propagation process of the ReLU activation function, a new loss calculation method is proposed. Firstly, the parameters of the pre-training model on the COCO data set are used as the initial parameters. Secondly, CE and CCE are used to calculate the loss during the calculation of loss in the back propagation. Finally, AReLU activation function is used and a weight assigned to CE and CCE is added as final loss to update the parameters. The experimental results show that this algorithm improves the convergence speed and accuracy of the model with respect to imbalanced data. Compared with other loss functions, ours method performance is state-of-the-art, such as complement cross entropy.