《Table 1 Parameter details of the proposed MSMLCNN》

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《Pedestrian attribute classification with multi-scale and multi-label convolutional neural networks》

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First，it is very difficult to assign suitable weights to different attribute cost functions that are connected with different layers in the training progress.Second，attributes having complex localizing characteristics and different scales，the way only using features learned in the last layer is inappropriate，because the features learned in the last layer are too global to local attributes（e.g.，has sunglasses，upper Body VNeck and footwear Sandals）.Therefore，as shown in Fig.2，in the proposed MSMLCNN，supervisory signals are added by fully connecting each attribute with multiple pooling layers at different scales，which makes the MSMLCNN able to learn multi-scale features for different attributes and apply multi-scale features for the testing phase.Parameter details of the proposed MSMLCNN is listed in Table 1.In the table C，MP and AP represent convolutional，max pooling and average pooling layers，respectively.