I'm trying to implement the vehicle re-identification model described in https://arxiv.org/pdf/2004.06271.pdf.
My question focuses on Section 3.2 of the paper, which uses a ResNet-50 for deep feature extraction in order to generate discriminative features which can be used to compare images of vehicles by Euclidean distance for re-identification. It takes a 256x256x3 image as input.
My understanding of ResNet-50 is that its output is of the shape N, where N is the number of classes which an input image could be, and ground truth labels take the form of a one-hot encoding where the '1' value represents the node in the output layer which is associated with the given class.
I am therefore confused by the usage of ResNet-50 in a re-identification task in which the goal is to generate an array of discriminative features which can be compared by Euclidean distance. There is no discrete set of N classes, as the model should work on any of the infinite number of vehicles in the world.
What is the ground truth label in a ResNet-50 in the context of a re-identification task?