Abstract: In order to solve low classification precision caused by the lack of training data or the
classification performance constraint of single convolutional network model, a fine-grained category
classification algorithm based on data augmentation and multi-model ensemble is proposed. Firstly, the
paper designs a variety of data augmentation methods to increase the number of pictures in CompCars
dataset, including mirroring, rotation, multiscale scaling, Gaussian noise, random cropping and color
enhancement. Then 3 differentiated models, CaffeNet, VGG16 and GoogleNet, are trained using the
constructed differentiated dataset by different data sampling. A multi-layer ensemble learning method is
used to integrate multi-model’s classification results. The experimental results show the fine-grained
classification of the differentiated convolution network trained on the different datasets generated by the
different data augmentation method. The experiment also shows the classification results of multi-model
ensemble with different ensemble strategy. The final classification precision of multi-model ensemble is
94.9%. Compared with the best single model GoogleNet, the classification precision is increased 9.2%.
The results verify the effectives of proposed algorithm.

Key words: fine-grained category classification, data augmentation, convolutional network, ensemble learning