With the rapid growth in the number of clothing pictures on the Internet, the demand for classification of a large number of clothing is increasing. The traditional use of manual semantic attribute annotation of clothing images does not fully express the rich information in the clothing image, and the traditional hand-designed features can no longer meet the requirements of real precision and speed. In recent years, deep learning has been applied to all aspects of computer vision, laying a solid foundation for clothing classification and recognition technology based on deep learning. In this study, three new sub-datasets are constructed according to the existing dataset deepfashion, the deepfashionkid dataset for classification training, the deepfashionVoc dataset for training with Faster R-CNN, and the deepfashionMask dataset for Mask R-CNN training. The clothNet model is pre-trained on the VGG16 using the deepfashionkid dataset to obtain the clothNet model, which in turn improves the loss function of the Faster R-CNN. And each compares the difference between the two algorithms using clothNet pre-trained model and not used. In addition, this study adopts a new pre-training strategy to adopt a training method similar to grafting learning. Experiments show that these training techniques are helpful for improving the detection accuracy.