Horse Breed Classification Based on Transfer Learning

Expert identification of horse breeds is an age-old task that can now be identified using genetic techniques. However, neither approach is cheap nor efficient. The automatic classification of horse breeds by computer vision is an effective solution. In this paper, we solve this task by proposing a novel method using transfer learning of pre-trained deep convolution neural networks architectures. The pre-trained convolutional neural networks include MobilenetV2, Mobilenet, Xception, VGG16, and VGG19. We use the keras deep learning framework, and train these deep convolution neural networks for transfer learning, which overcomes the problem of small amount of data in the early stage. An extensive experimental study on various horse breeds datasets shows that our method obtains an average accuracy rate of automatic classification of horse breeds to 89.34%, which has obvious advantage over other deep convolutional neural network models such as xception, vgg16, vgg19 and self-made convolutional neural network.