Overlapping-echo detachment (OLED) planar imaging sequence can provide reliable T2 mapping within milliseconds even under continuous object motion. A detachment algorithm based on the sparsity and structure similarity constraints has been used to separate the echo signals to form T2 map. However, the effectiveness of separation is limited and the reconstruction is time consuming. Here, an end-to-end deep convolutional network based on deep residual network was introduced. The results of simulation and in vivo human brain show that it can reconstruct T2 mapping efficiently and reduce the reconstruction time from minutes to milliseconds after deep residual network is trained.