clean-fid - Assessing Generative Models with Standardized FID and KID Score Calculations

Clean-FID: Evaluating Generative Models Made Easier

Introduction

In the world of AI and machine learning, generative models are used to create data that mimic real-world data. One popular way to evaluate the performance of these models is through a metric known as the Fréchet Inception Distance (FID). However, calculating FID accurately can be tricky due to variations in image processing methods. Enter Clean-FID, a library designed to standardize these calculations, ensuring that comparisons across different models, studies, and research groups are fair and accurate.

What Clean-FID Offers

Clean-FID addresses the inconsistencies that arise from varied implementations of image quantization and resizing across different libraries. This tool helps create a uniform platform for FID calculation, reducing discrepancies that might skew the results.

Core Features

Standardized FID Calculation: Clean-FID provides a means to compute the FID score using consistent and reliable methods, ensuring that results are comparable across different implementations.
CLIP-FID Support: Recently, Clean-FID began supporting FID calculations using CLIP features, enabling even more accurate assessments of generative models in terms of image-text relations.
JPEG Compression Handling: The library takes into account the effects of JPEG compression on FID scores, offering a more precise evaluation method.
Precomputed Dataset Statistics: Clean-FID provides precomputed statistics for several well-known datasets, streamlining the process of FID calculation.
Custom Dataset Statistics: Users can also create custom statistics tailored to their specific datasets, further enhancing the flexibility of this tool.

Quick Start Guide

Installation: To install Clean-FID, simply run:
```
pip install clean-fid
```

Computing FID Between Two Folders:

from cleanfid import fid
score = fid.compute_fid("folder1", "folder2")

Using Precomputed Statistics: Clean-FID allows you to compute FID using precomputed dataset statistics with a simple command, for instance:
```
score = fid.compute_fid("generated_images", dataset_name="ffhq", dataset_res=256)
```

Backward Compatibility

Clean-FID ensures compatibility with previous methods used in legacy PyTorch and TensorFlow implementations of FID, making it easier to transition to the more accurate methods proposed by this library.

Conclusion

For researchers and developers working with generative models, Clean-FID offers a robust and streamlined method to calculate FID scores, addressing many of the subtleties and potential pitfalls of traditional methods. By standardizing this vital aspect of model evaluation, Clean-FID makes it easier to compare results across different studies and contributes to more consistent research outcomes in the field of generative modeling.