What is the evaluation metric used in image models known for measuring quality?

The evaluation metric known for measuring the quality of image models is the Fréchet Inception Distance (FID). This metric provides a statistical measure of how similar a generated image distribution is to a set of real images. Calculating FID involves comparing the features of the images extracted from a pre-trained Inception network, which means it takes into account both the mean and the covariance of the feature representations.

FID is particularly useful because it aligns more closely with human perception of image quality. Unlike simpler metrics that may focus on pixel-wise differences, FID captures the distribution of features and can effectively assess how realistic and diverse the generated images are. Lower FID scores indicate that generated images are more similar to the real images in the training set, highlighting the quality of the generative model.

Other metrics, such as mean squared error, precision, or recall, while useful in various contexts, do not specifically address the complex nature of image quality in generative models. Mean squared error typically evaluates numerical accuracy rather than perceived image quality, while precision and recall are more applicable to classification tasks and might not give a comprehensive view of image generation quality. Hence, FID stands out as the relevant metric in this context.