Abstract In this paper, a novel deep architecture named BraidNet is proposed for person re-identification. BraidNet has a specially designed WConv layer, and the cascaded WConv structure learns to extract the comparison features of two images, which are robust to misalignments and color differences across cameras. Furthermore, a Channel Scaling layer is designed to optimize the scaling factor of each input channel, which helps mitigate the zero gradient problem in the training phase. To solve the problem of imbalanced volume of negative and positive training samples, a Sample Rate Learning strategy is proposed to adaptively update the ratio between positive and negative samples in each batch. Experiments conducted on CUHK03- Detected, CUHK03-Labeled, CUHK01, Market-1501 and DukeMTMC-reID datasets demonstrate that our method achieves competitive performance when compared to stateof-the-art methods