An image recognition method based on transfer learning

Traditional neural networks need a lot of data set training in feature extraction and image classification tasks, and classification tasks are often not completed in small sample tasks. This paper uses transfer learning methods to design the traditional network VGGNet-16 to avoid the need to train the network from scratch Complexity. According to the classification target, VGGNet-16 is fine-tuned to obtain the network used for this classification task. The effect of different Dropout values on the model is verified in the fully connected layer. The results show that migration learning has excellent training effects for small data samples and reduces training costs. After adding the Dropout layer, the performance of the network model is not only improved by 1.1 %, but also more stable than the network model without the Dropout layer. The data shows that the network model is more stable when the dropout is 0.5 and the batchsize is 32, and the accuracy rate reaches 97.10%.