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Aiming at the problem of lengthy manual calibration of multi-target data sets in the field of computer vision,a rapid automatic labeling method based on Mask Scoring R-CNN was proposed. Firstly, a fast automatic labeling framework for high quality data sets is designed. Then, the automatic labeling model of multiple target data is trained and classification and labeling system is built. Secondly, the quality evaluation method of multiple target mask labeling based on MaskIoU Head was further studied based on the comparison of different residual networks and the introduction of transfer learning. Besides,the rapid automatic label method for high quality multiple target data set based on Mask Scoring R-CNN is proposed.Finally, taking vehicle data as an example, the experimental results show that Mask Scoring R-CNN has the advantages of good target data classification effect and high Mask segmentation accuracy compared with the Mask R-CNN and Faster R-CNN. The detection accuracy of the proposed method is 93.4%, and the label speed is 95.77% higher than manual label.