The present invention relates to a method for learning class descriptors for the detection and the automatic location of objects in a video, each object belonging to a class of objects from among a set of classes, the method using: a learning base, composed from reference videos and containing annotated frames each comprising one or more labels identifying each object detected in the frames, descriptors associated with these labels and learned previously by a preprocessing neural network from the annotated frames of the learning base, an architecture of neural networks defined by parameters centralized on a plurality of parameter servers, and a plurality of computation entities working in parallel, a method in which, for each class of objects, one of the neural networks of the architecture is trained by using as input data the descriptors and the labels to define class descriptors, each computation entity using, for the computation of the class descriptors, a version of the parameters of the parameter server on which the entity depends, and returning to this parameter server the parameters updated at the end of its computation, and the parameter servers exchanging with one another the parameters of each computation entity for the training of the neural networks for each class descriptor.