This repository implements the LPIPS metric, a perceptual similarity metric for comparing images. The LPIPS metric is widely used in image generation and restoration tasks to evaluate the perceptual quality of images.
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- VGG-based Feature Extraction: Uses a pre-trained VGG16 model to extract multi-level features.
- Learned Linear Layers: Applies learned weights to compute perceptual similarity.
- Pre-trained Weights: Includes pre-trained weights for LPIPS computation.
- Utility Functions: Includes helper functions for downloading and verifying model checkpoints.
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Clone the repository:
git clone https://github.com/ProgramerSalar/LPIPS.git cd LPIPS -
Install the required dependencies:
pip install -r requirements.txt
You can compute the perceptual distance between two images using the LPIPS class:
import torch
from lpips import LPIPS
# Create sample images
img1 = torch.randn(1, 3, 256, 256)
img2 = torch.randn(1, 3, 256, 256)
# Compute perceptual distance
lpips = LPIPS().eval()
distance = lpips(img1, img2)
print(f"Perceptual distance: {distance.item():.4f}")To use the pre-trained LPIPS model:
from lpips import LPIPS
lpips_model = LPIPS.from_pretrained('vgg_lpips')
distance = lpips_model(img1, img2)
print(f"LPIPS distance: {distance.item():.3f}")Lpips/
├── .gitignore
├── assets/ # Folder for storing images and other assets (ignored by Git)
│ ├── download (1).jpeg
│ ├── download.jpeg
│ └── lpips_example.png # Example image for the Readme (if added)
├── lpips.py # Implements the LPIPS model and its components
├── utils.py # Utility functions for downloading and verifying model checkpoints
├── vgg.pth # Pre-trained weights for the LPIPS model
├── example.txt # Example file for testing purposes
├── requirements.txt # Dependencies for the project
└── Readme.md # Project documentation
This project is licensed under the MIT License. See the LICENSE file for details.