Food Image Detection Using CNN Algorithm

Abstract

In the coming years, the fusion of deep learning with computer vision will usher in significant advancements in various domains, including food image analysis. A pioneering approach will be introduced to meet the rapidly increasing demand for automated recipe generation from food images, leveraging state-of-the-art deep learning techniques. The proliferation of social media platforms and the omnipresence of food-related content will necessitate efficient algorithms capable of understanding food images and generating coherent recipes. The proposed framework will integrate deep learning neural networks for robust food image recognition and sequence-to-sequence models for recipe generation. Initially, a pre-trained model will be employed on extensive food image datasets to extract pertinent features, followed by fine-tuning on domain-specific datasets to enhance accuracy. This pre-training step will enable the network to learn rich representations of various food items and their compositions. Subsequently, a sequence-to-sequence model, such as an encoder-decoder architecture equipped with attention mechanisms, will be employed to map the extracted image features to textual recipes. This model will learn to generate coherent and contextually relevant recipes from input food images, capturing the intricate details of different cuisines and dishes. Key Words: Deep Learning, Food Image Analysis, Recipe Generation, Convolutional Neural Networks.