Abstract: Aiming at the problem that vehicle models are difficult to recognize due to differences in vehicle posture and viewing angles, a vehicle model recognition network based on progressive multi-granularity ResNet is proposed. Firstly, a progressive multi-granularity local convolution module is proposed by using the ResNet network as the backbone network to perform local convolution operations on vehicle images of different granularity levels, so that local features of vehicles at different granularity levels can be paid attention to when the network is reconstructed. Secondly, for the multi-granularity local feature map, the random channel discarding module is adopted to perform random channel discarding, which suppresses the network's attention to the vehicle's salient regional features and improves the attention of non-salient features. Finally, a progressive multi-granularity training module is proposed. A classification loss is added in each training step to guide the network to extract more discriminative and diverse vehicle multi-scale features. Experimental results show that the recognition accuracy of the proposed network reaches 95.7%, 98.8%, and 97.4% respectively on the Stanford-cars dataset, the Compcars network dataset, and the vehicle model dataset VMRURS in real scenes. In comparison with the comparative network, the proposed network not only has higher recognition accuracy but also has better robustness.