Abstract: Mammography is internationally recognized as an effective screening tool for early breast cancer. This paper proposes a mammographic mass detection method based on YOLOv3 network. The method could complete mass detection of the whole image at a faster speed while ensuring accuracy. By applying transfer learning technology, the mass lesion detection knowledge learned from the digitized mammograms were transferred to the full-field digital mammograms, which effectively solved the current lack of full-field digital mammography datasets. The five-fold cross-validation method was used for evaluation based on DDSM and INbreast datasets. Through extensive experiments, the obtained average accuracy of the mass detection over the five folds is 81.34%.