New pictures of current classes are always arriving in open-ended continuous learning, and new classes are constantly
appearing. Due to the great generalization capacity which was before deep learning networks, transfer learning was utilized to
identify the most effective network for feature extraction from food photos. During transfer learning, it leverages online data
augmentation to make up for the paucity of datasets from other orientations.
