The ReidentificationNet model, generates embeddings to identify objects captured in different scenes.
The model is essentially a ResNet50 backbone which takes in cropped images of objects as input produces feature embeddings as output.
The training algorithm optimizes the network to minimize the triplet, center and cross entropy loss.
The primary use case intended for this model is to generate embeddings for an object and then perform similarity matching across embeddings from different scenes.
The datasheet for the model is captured in its model card hosted at NGC.