Diffusion models, which have gained attention for their ability to generate high-quality images and other data types, are often benchmarked using several well-known datasets. These datasets serve as standard references for evaluating the performance of models across different tasks and domains. Some of the common datasets include CIFAR-10, CelebA, and ImageNet. Each of these datasets is used to assess specific aspects of model performance, such as image quality and diversity.
CIFAR-10 is frequently used for benchmarking because it contains 60,000 images categorized into ten different classes, such as animals and vehicles. This dataset is particularly useful for evaluating diffusion models in scenarios where a smaller, more manageable image size is needed, as each image is just 32x32 pixels. Because of its simplicity, CIFAR-10 allows developers to easily compare the performance of their models against established results. Additionally, it provides a straightforward way to analyze how diffusion processes work in generating images that resemble those in the dataset.
CelebA, which consists of more than 200,000 celebrity images with various attributes, is employed to assess the model's ability to generate detailed and varied human faces. The rich variety in this dataset helps in fine-tuning models to capture intricate features and generate realistic images. Finally, ImageNet, with over 14 million images across thousands of categories, is widely used for its complexity. It challenges diffusion models to produce diverse and high-quality outputs across a broad spectrum of subject matter. In summary, CIFAR-10, CelebA, and ImageNet are essential datasets that help developers benchmark and compare the performance of diffusion models effectively.