This paper addresses the challenges in person Re-Identification (ReID) by proposing a new dataset, MSMT17, and a Person Transfer Generative Adversarial Network (PTGAN). MSMT17 is designed to simulate real-world scenarios with complex scenes, lighting variations, and a large number of identities. It contains 126,441 bounding boxes and 4,101 identities, making it the largest and most challenging public dataset for person ReID. The paper also introduces PTGAN, which aims to bridge the domain gap between different datasets by transferring person images from one dataset to another while preserving identity information. Extensive experiments demonstrate that PTGAN effectively reduces the domain gap, improving the performance of person ReID models trained on one dataset when tested on another. The proposed methods are evaluated on several datasets, showing significant improvements in accuracy and robustness to domain differences.This paper addresses the challenges in person Re-Identification (ReID) by proposing a new dataset, MSMT17, and a Person Transfer Generative Adversarial Network (PTGAN). MSMT17 is designed to simulate real-world scenarios with complex scenes, lighting variations, and a large number of identities. It contains 126,441 bounding boxes and 4,101 identities, making it the largest and most challenging public dataset for person ReID. The paper also introduces PTGAN, which aims to bridge the domain gap between different datasets by transferring person images from one dataset to another while preserving identity information. Extensive experiments demonstrate that PTGAN effectively reduces the domain gap, improving the performance of person ReID models trained on one dataset when tested on another. The proposed methods are evaluated on several datasets, showing significant improvements in accuracy and robustness to domain differences.