Research on flower classification technology based on VGG16 neural network

Flower classification is an important branch in the field of plant classification. Flower images are difficult to improve recognition accuracy due to the similarity of. In recent years, with the continuous development of deep learning convolutional neural networks, convolutional neural networks have been increasingly used in flower image classification. However convolutional neural networks have a large number of model parameters, parameter redundancy, and are difficult to deploy on mobile devices. To solve the above problems, this paper the following work: 1. Training the flower dataset on the convolutional neural network model VGG16. 2. Using transfer learning, the results obtained training are trained on the pruned model. 3. Setting different pruning rates, retraining the model to get the final result. Experiments show that using pruning algorithm to train the flower dataset improves accuracy, with the highest accuracy being 93.10%.