Research on animal image classification based on transfer learning
Man Hu, Fucheng You · Proceedings of the 2020 4th International Conference on Electronic Information Technology and Computer Engineering · 2020
Training a convolutional neural network requires a large number of sample data and a large amount of computing power. In some practical application scenarios, there may be difficulties in sample data collection and complex network model construction. To improve the classification accuracy and fitting speed of the convolutional neural network, a transfer learning classification method for an animal image is proposed. The fully connected layer of the pre-trained ResNet18 network is modified, and the eight animals in the animal-10 dataset on Kaggle are used to fine-tune the network model. The best classification accuracy of the obtained network model for a single animal is 97%, and the classification accuracy for all animals is 92%. Compared with the model that did not use transfer learning training, this kind of transfer learning network model has a great improvement in accuracy and fitting speed.