From Image to Recipe: Transfer Learning for Identifying Food Ingredients

Although there has been a lot of advancement in the use of AI in food technology, ingredient detection is still difficult since food textures and looks vary so much. A potential remedy is transfer learning, which leverages prior knowledge to improve performance on particular datasets by customizing pre-trained models for novel tasks. The use of deep learning to analyses food images has attracted a lot of attention lately, especially because of its potential to transform the culinary and nutritional sciences. The effectiveness of transfer learning models, such as Xception, ResNet50, and EfficientNet, for detecting components from food photos is investigated in this work. The background research emphasizes the developments in convolutional neural networks and their efficacy in a range of picture classification tasks, highlighting the necessity of precise ingredient identification systems in applications such as recipe suggestion and nutritional monitoring. Using a carefully chosen collection of food photos labelled with matching ingredients, the process comprises optimizing pre-trained models. The design of each model is customized to address the multi-label categorization issue that arises during the identification of ingredients. Techniques like data augmentation are used to train and evaluate the models. The findings show that ResNet50 with 92% and Xception with an accuracy of 85% obtain the maximum accuracy. EffectiveNet variations also exhibit good performance, with top-1 accuracy reaching 70%, especially eff6 and eff7. In summary, the research finds that transfer learning models ResNet50 and Xception in particular are very successful in identifying ingredients, opening the door to reliable, practical uses in nutrition analysis and food technology.