Food Recognition Using Extreme Learning Machines

Abstract

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. Experimental research has demonstrated that the model's capability to categorize food photos from various potential orientations has been greatly improved by online data augmentation. Second, this study effort decreases the dimensions of retrieved features by using the Relief F technique to rank features. The redundant characteristics make the model's computations more difficult. The best epoch is achieved by getting a training accuracy of 98 percent and a validation accuracy of 92 percent.