The loss model with class variance for fine-grained classification

We propose a loss model with class variance for fine-grained image classification. It adopts basic convolutional neural network to get features. The dates from dataset are shuffle selected as inputs according to batch size and their outputs are processed by attention model. Because of class variance in the same class is smaller and that in the different class is larger, in the training phase, we use class variance to define the loss function. The total loss model combines the loss function with class variance and label loss function. Both are jointly employed to fast convergence. Compared with state-of-the-art methods, experimental results demonstrate our model has better performance.