We develop a new edge detection algorithm, holistically-nested edge detection (HED), which performs image-to-image prediction by means of a deep learning model that leverages fully convolutional neural networks and deeply-supervised nets. HED automatically learns rich hierarchical representations (guided by deep supervision on side responses) that are important in order to resolve the challenging ambiguity in edge and object boundary detection. We significantly advance the state-of-the-art on the BSD500 dataset (ODS F-score of .790) and the NYU Depth dataset (ODS F-score of .746), and do so with an improved speed (0.4s per image). Detailed description of the system can be found in our paper. If you have downloaded the previous version (testing code) of HED, please note that we updated the code base to the new version of Caffe. We uploaded a new pretrained model with better performance. We adopted the python interface written for the FCN paper instead of our own implementation for training and testing. The evaluation protocol doesn't change.