Multiscale features have attracted widespread attention in person reidentification due to their capability to enhance the model information processing. However, the use of multiscale features often leads to model redundancy and low operational efficiency, greatly affecting the practical application of person reidentification. To address the above issue, we propose an efficient and lightweight multiscale network for person reidentification. Specifically, we construct a baseline network for lightweight person reidentification called the multiscale efficient network (MSENet). It comprises three primary stages, with each stage employing the multiscale efficient block, consisting of varying numbers of lightweight efficient convolution blocks. The proposed network efficiently achieves pedestrian image retrieval while maintaining low model complexity. Subsequently, we propose a lightweight pyramid feature fusion module to aggregate multilayer features of the MSENet, enhancing feature diversity and obtaining robust features. Finally, we design a contour branch that focuses on the overall feature extraction of pedestrian images, effectively reducing the interference of background information. Extensive experiments conducted on three popular datasets have demonstrated that the proposed method has excellent recognition accuracy and low model complexity.