An Improved Vehicle Fine-Grain Identification Algorithm Based on Stochastic Weight Average

Abstract

In the field of motor vehicle recognition, the use of neural network models has become the standard, and the tuning of hyperparameters and loss functions has been shown to be an effective way to improve the performance of these models. However, when using classical convolutional network architectures (e.g., ImageNet) and training them on motor vehicle images with random labels, the overparameterization problem can lead to suboptimal results and an increased risk of recognition failure. P. Ismailova et al. proposed a solution to this problem with the use of weight averaging, which resulted in the development of the simple and effective Stochastic Weight Averaging (SWA) optimizer. In this paper, we apply the SWA method to optimize the original recognition model and demonstrate significant improvements in accuracy through the use of different learning rate schemes with various traditional optimizers. We also identify suitable hyperparameter values to enhance the model's generalization abilities through several experiments, reducing the waste of resources in the motor vehicle recognition task and improving the recognition accuracy of fine-grained images in general, thus increasing the efficiency of related fields.