Project Introduction: PyTorch-FID
PyTorch-FID is a project that provides an implementation of the Fréchet Inception Distance (FID) in PyTorch. This tool is a translation of the original TensorFlow-based implementation offered by the Institute of Bioinformatics at JKU Linz. The main role of FID is to assess the similarity between two image datasets, making it a widely employed metric for gauging the quality of images generated by Generative Adversarial Networks (GANs).
Understanding FID
The Fréchet Inception Distance, or FID, is a comparative measure that evaluates how similar two groups of images are by measuring the distance between two statistical distributions. Specifically, it uses the Fréchet distance to compare two Gaussians fitted to feature representations which are extracted using the Inception network. The FID score is well-regarded because it aligns closely with human evaluations of image quality.
How It Works
PyTorch-FID computes the FID score between two collections of images. Researchers or developers often use it to determine how closely a set of generated images matches real images. Importantly, while the weights and model architecture in PyTorch-FID are identical to the original TensorFlow implementation, slight differences may arise in the calculated FID due to varying image processing techniques between libraries.
Installation and Requirements
To start using PyTorch-FID, you can install it easily with pip:
pip install pytorch-fid
The project requires the following software:
- Python 3
- PyTorch
- Torchvision
- Pillow
- NumPy
- SciPy
Usage
Calculating the FID score using PyTorch-FID is straightforward. Users can run the following command, indicating the paths to the two image datasets:
python -m pytorch_fid path/to/dataset1 path/to/dataset2
For users wishing to leverage GPU capabilities, specifying the GPU index with --device cuda:N is possible.
Customizing Feature Layers
One of the features that sets PyTorch-FID apart is the ability to specify which layer of the Inception network you'd like to use for generating feature maps. By default, the pool3 layer is used. You can change this by using the --dims N flag to specify layers with smaller dimensions, adjusting according to the number of images you have or for other experimental needs.
Creating and Using .npz Archives
In many cases, you may wish to compare multiple models against a standard dataset repeatedly. To avoid recalculating each time, PyTorch-FID allows users to store calculated statistics in a .npz file using the --save-stats option. This method lets you directly use the archive for future comparisons, saving on computation time.
Citation and Licensing
Should you utilize PyTorch-FID in your work, consider citing it as follows:
@misc{Seitzer2020FID,
author={Maximilian Seitzer},
title={{pytorch-fid: FID Score for PyTorch}},
month={August},
year={2020},
note={Version 0.3.0},
howpublished={\url{https://github.com/mseitzer/pytorch-fid}},
}
The PyTorch-FID implementation is offered under the Apache License 2.0, keeping it aligned with the licensing of the original FID introduction paper and its TensorFlow-based implementation.