Perceptual Similarity Project Introduction
Overview
The Perceptual Similarity project focuses on evaluating the similarity between images using advanced deep learning techniques. This involves a perceptual metric known as LPIPS (Learned Perceptual Image Patch Similarity) and a supporting dataset called BAPPS (Berkeley-Adobe Perceptual Patch Similarity). This work is based on the research conducted by Richard Zhang, Phillip Isola, Alexei A. Efros, Eli Shechtman, and Oliver Wang, and was presented in the CVPR conference in 2018.
The LPIPS Metric
What is LPIPS?
LPIPS stands for Learned Perceptual Image Patch Similarity. It’s a metric designed to measure the distance between image patches. A higher LPIPS score indicates that the images are more different, while a lower score suggests they are more similar. This metric is particularly useful because it utilizes deep network activations that perform reliably across different network architectures.
Basic Usage
To get started with LPIPS, you can install it via Python using pip install lpips. Here's a simple usage example:
import lpips
loss_fn_alex = lpips.LPIPS(net='alex')
img0 = torch.zeros(1,3,64,64) # An image in RGB format, normalized to [-1,1]
img1 = torch.zeros(1,3,64,64)
d = loss_fn_alex(img0, img1)
This code snippet will compute the distance between two images using the AlexNet variant of the LPIPS metric.
How it Works
The LPIPS metric utilizes various deep network architectures like AlexNet and VGG to extract features that are then used to determine perceptual similarity. By default, the metric uses AlexNet, which is optimized for both performance and speed.
BAPPS Dataset
What is BAPPS?
The Berkeley-Adobe Perceptual Patch Similarity (BAPPS) dataset provides the necessary data for training and evaluating perceptual similarity models, like LPIPS. It includes two types of evaluations: Two Alternative Forced Choice (2AFC) and Just Noticeable Differences (JND).
How to Use BAPPS
To use the BAPPS dataset, one can download it using the provided scripts. These datasets are crucial for evaluating and training models under different perceptual conditions like image distortion, color adjustment, and frame interpolation challenges.
Evaluating with BAPPS
The BAPPS dataset is designed to help test and refine perceptual similarity metrics. Evaluators in the 2AFC framework compare a reference image with two distorted alternatives to judge which is more similar to the reference. In JND, the task is to determine whether two images are identically similar or different.
Implementation and Training
The project repository provides scripts for not only evaluating but also for training new models using the BAPPS dataset. This enables researchers to fine-tune their models to achieve better perceptual similarity results.
Conclusion
The Perceptual Similarity project highlights the effectiveness of deep learning features as a perceptual metric, demonstrated through the LPIPS metric and BAPPS dataset. This project is an invaluable resource for researchers working on image similarity and image processing tasks, providing tools and datasets that facilitate more accurate and efficient evaluations.