LPIPS (Learned Perceptual Image Patch Similarity)

LPIPS is a Python library that implements the Learned Perceptual Image Patch Similarity metric. This metric is designed to measure the similarity between two images in a way that aligns more closely with human perception than traditional metrics like MSE or SSIM. It leverages deep features extracted from pre-trained convolutional neural networks (like AlexNet, VGG, or SqueezeNet). The library is currently at version 0.1.4 and is primarily maintained through its GitHub repository, with releases tied to significant updates. It's often used in image generation and restoration tasks to evaluate perceptual quality or as a perceptual loss function.