The spectrum of data-driven fault diagnosis models is greatly expanded by deep learning. However, classical convolution and multiple branching structures have their faults in computational complexity and feature extraction. To address these issues, we propose an improved re-parameterized visual geometry group (VGG) network (RepVGG) for rolling bearing fault diagnosis. In order to meet the requirements of neural networks for the amount of data, data augmentation is performed to increase the amount of original data. Then, the original one-dimensional vibration signal is processed into a single-channel time-frequency image using the short-time Fourier transform and converted into a three-channel color time-frequency image using pseudo-color processing technology. Finally, the RepVGG model with an embedded convolutional block attention mechanism structure is developed to extract defect features from three-channel time-frequency images and perform defect classification. Two datasets of vibration data from rolling bearings are used to demonstrate the strong adaptability of this method compared with other methods.