  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, i.e.
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.
