Through the comprehensive experiments demonstrate that the multi-branch and multi-scale learning network, MMAL-Net, has good classification ability and robustness for images of different scales and can achieves state-of-the-art results on CUB-200-2011, FGVC-Aircraft and Stanford Cars datasets.