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This paper introduces our solution for the Track2 in AI City Challenge 2021 (AICITY21). The Track2 is a vehicle re-identification (ReID) task with both the real-world data and synthetic data. We mainly focus on four points, ie training data, unsupervised domain-adaptive (UDA) training, post-processing, model ensembling in this challenge.(1) Both cropping training data and using synthetic data can help the model learn more discriminative features.(2) Since there is a new scenario in the test set that dose not appear in the training set, UDA methods perform well in the challenge.(3) Post-processing techniques including re-ranking, image-to-track retrieval, inter-camera fusion, etc, significantly improve final performance.(4) We ensemble CNN-based models and transformer-based models which provide different representation diversity. With aforementioned techniques, our method finally achieves 0.7445 mAP score, yielding the first place in the competition. Codes are available at https://github. com/michuanhaohao/AICITY2021_Track2_DMT.