Long-tail distribution image data identification method based on dual-channel learning

The invention discloses a long-tail distribution image data identification method based on dual-channel learning. The method comprises the following steps: 1) constructing a dual-channel learning model combining unbalanced learning and small sample learning; 2) updating all parameters in the dual-channel learning model by utilizing dual-channel learning total loss and back propagation, and storingoptimal dual-channel learning model parameters; and 3) inputting the image data of the test set to the optimal dual-channel learning model, and obtaining the prediction label of the image. Accordingto the invention, unbalanced learning and small sample learning are combined to solve the problem of long-tail distribution image data identification; the unbalanced learning channel can improve the identification accuracy of the unbalanced data set; the small sample learning channel can improve the feature representation of model learning, and the dual-channel total loss enables the model to focus on the unbalanced learning channel in the early stage of training and focus on the small sample learning channel in the later stage of training, thereby improving the recognition accuracy of the long-tail distribution image data on the whole.