近年來，受惠於硬體效能的強大以及深度學習相關研究的進步，在電腦視覺衍生出許多實用的應用，且已經與我們的生活息息相關了。然而，隨著 person re-identification (re-ID) 的快速發展，這個技術可應用在多個場景，例如尋人系統、無人商店、輔助人臉辨識等等。研究的領域上，有許多人提出新的網路架構，其中採用 ResNet 的 Batch DropBlock (BDB) 網路架構在 person re-ID 任務中獲得良好的成績。本論文受 ResNeSt 啟發，因此我們採用最先進的 ResNeSt 當作特徵擷取器以及調整網路的維度，期望在 BDB 中有更好的表現。實驗中，我們採用 Market-1501、CUHK03-Label、CUHK03-Detect、DukeMTMC-reID 四種常見的 person re-ID 資料集進行實驗。實驗表明，我們設計的網路架構與 BDB 相比，評估指標 Rank-1 與 mAP 獲得更好的結果。

In recent years, benefited from the powerful hardware performance and the advance of related deep learning research. Computer vision has derived many practical applications and has been closely related to our lives. However, following the rapid development of person re-identification (re-ID), this technology can be applied in many scenarios, such as person tracking systems, unmanned stores, assisted face recognition, etc. In the field of research, many new network architectures have been proposed, among which the Batch DropBlock (BDB) Network using ResNet has obtained good results in the person re-ID task. This paper is inspired by ResNeSt, so we use the state-of-the-art ResNeSt as a feature extraction and adjust the dimension of the network, expecting a better performance in BDB Network. In the experiment, we use four common person re-ID datasets for experiments, such as Market-1501, CUHK03-Label, CUHK03-Detect, and DukeMTMC-reID. The experiments show that our designed network architecture obtains more accurate results with evaluation metrics the Rank-1 and the mAP compared to BDB Network.

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