This paper describes the participation of the the MRIM research Group of the LIG laboratory in the ImageCLEF scalable concept image annotation subtask 1. We made use of a classical framework to annotate the 500K images of this task: we tuned an existing Convolutional Neural Network model to learn the 251 concepts and to locate bounding boxes of such concepts, and we applied a specific process to handle faces and face parts. Because of time constraints, we fully processed 35% of the full corpus (i.e. 180K images), and partially the remaining images of the corpus. For our first participation to this task, the results obtained show that we have to manage the localization in a more effective way.