The Food-101 Data Set

The Food-101 dataset is a comprehensive collection designed for food image classification and recognition tasks. It contains 101 food categories, with a total of 101,000 images. Each category includes 750 training images and 250 manually reviewed test images, providing a well-balanced dataset for training and evaluating machine learning models.

The training set intentionally includes some noise, such as intense colors and occasional incorrect labels, to simulate real-world data and challenge model robustness. All images have been rescaled to a maximum side length of 512 pixels to ensure uniformity in size.

Key Features:

• 101 Food Categories: Covering a diverse range of food types for classification tasks.
• 101,000 Images: A large, rich dataset with both training and test images.
• Real-World Noise: Includes training images with some noise, enhancing model generalization.
• Rescaled Images: All images have been rescaled to a maximum side length of 512 pixels for consistency.