Abstract: Pedestrian Re-identification (RE-ID) concentrates on identifying specific pedestrians in cross-domain cameras. At present, most RE-ID algorithms study methods that enhance feature extraction ability, and various models appear, but they all have problems such as high model complexity or weak recognition ability. To solve these problems, BagTricks, a concise Re-ID benchmark model, was combined with channel attention mechanism to improve the ability of the model to extract significant features. Meanwhile, Circle loss was added to improve the loss function. Experiments on three popular Re-ID dataset show that this model obtains rank-1 accuracy of 95.6% and mAP of 88.5% on the mainstream image re-identification Market1501 dataset, and achieves the rank-1 accuracy of 89.1% and 76.7% on DukeMTMC and CUKE03 dataset. This method improves the accuracy of the model, and is easy to implement, and achieves competitive performance, which is better than most existing methods.