At each step of the training process, NeurEco records a model into the checkpoint. It is possible to explore the recorded models via the load_model_from_checkpoint function of the python API. Sometimes an intermediate model in the checkpoint can be more relevant for targeted usage than the final model with the optimal precision (for example if it gives a satisfactory precision while being smaller than the final model with the optimal precision and thus can be embedded on the targeted device).