In this research project

we created a food image classifier using machine learning. However

training an image classifier from scratch has many complications. It is difficult to acquire a vast amount of data to train an accurate model

and often the training process is very slow. We were able to overcome these difficulties by utilizing transfer learning on the RESNET-18 deep neural network as a feature extractor. The accuracy of our model increased from 6% to 60% after we employed transfer learning

while the training time decreased by half. The benefits of transfer learning were evident throughout this research

as it made our model more efficient in training while improving accuracy.