Abstract: Fine-grained image Classification is an important task in computer vision. The main challenge of the task are that intra-class similarity is large and that training data points in each class are insufficient for training a deep neural network. Intuitively, if we can learn more discriminative features and more detailed features from fined-grained images, the classification performance can be improved. Considering that channel attention can learn more discriminative features, spatial attention can learn more detailed features, this paper proposes a new spatial attention mechanism by modifying Squeeze-and-Excitation block, and a new mixed attention by combining the channel attention and the proposed spatial attention. Experimental results on two small-sample fine-grained image classification datasets demonstrate that on both VGG16 network and ResNet-50 network, the proposed two attention mechanisms achieve good performance, and outperform other referred fine-grained image classification methods.