The classification of food images is a significant challenge within the domain of computer vision and deep learning. This is driven by the growing demand for automation and precision in food-related industries such as health, nutrition, retail, and hospitality. The ability to accurately classify food or food images can enable applications like calorie estimations, dietary tracking, automated menu recommendations, and even food waste management.
Food image classification is a complex task due to high intra-class variance, inter-class similarity, and dataset size and diversity. Images within a single food category can vary greatly depending on lighting and presentation. There might also be similarities between different food types such as pasta and noodles or soups and stews. Large datasets such as this one, Food-101, contain diverse images that require preprocessing and effective modeling to achieve successful classification.
The goal of this project is to develop a deep learning-based classification model capable of recognizing and classifying images of 10 distinct food types. The assigned food types are:
- Beignets
- Breakfast Burrito
- Clam Chowder
- Crème Brulee
- Croque Madame
- Cupcakes
- French Onion Soup
- Hummus
- Pad Thai
- Shrimp and Grits
The approach towards this problem is crucial in ensuring seamless processing and efficient model building and evaluation.
- Data Loading and Preprocessing
We begin by loading and preprocessing the data to ensure it's in a suitable format for training, validation, and testing. We use
ImageDataGeneratorfor data augmentation, resizing, and normalizing the images.