Traditional garment retrieval methods use fixed-shape receptive fields, and they cannot extract features effectively when the garment target has geometric deformation. To solve this problem, we propose a garment retrieval method based on deformable convolution and similarity learning. Firstly, we build a deformable convolutional network which can automatically learn the sampling locations of garment features and the Hash code of garment images. Secondly, a similarity learning network is cascaded to measure the similarity of the Hash code. Finally, we obtain the retrieval results according to similarity scores. Experimental results show that this method can effectively extract the features of garment objects with geometric deformation, thus reducing the impact of image background features and improving the accuracy of the retrieval model.