A method of ship detection for GaoFen-2 (GF2) imagery is proposed based on deformable part model (DPM) and the comparison with the regional convolutional neural network (R-CNN) is carried out. The GF2 images are firstly segmented to obtain the rough regions of interest (ROI) of ships. Then the histogram of oriented gradients (HOG) features and multi-layer convolutional features are computed within the ROIs. The HOG and convolutional features are then adopted by the DPM and the R-CNN respectively. To test the best performance of the R-CNN, a shallower network (ZF-net) with five convolutional layers and a deeper one (VGG-net) with 13 convolutional layers are applied to the ship detection. The experiments results using eight GF2 images with 3523 ships show that the DPM and the R-CNN can detect the ships surrounded by water with a high recall rate and precision. However, for the ships staying together and surrounded tightly by other ships, the DPM performs better than the R-CNN. The average precision of the methods based on HOG+DPM, ZF-net and VGG-net are 95.031%, 93.282% and 93.683% respectively.