How to Implement the Frechet Inception Distance (FID) for Evaluating GANs
Last Updated on October 11, 2019
The Frechet Inception Distance score, or FID for short, is a metric that calculates the distance between feature vectors calculated for real and generated images.
The score summarizes how similar the two groups are in terms of statistics on computer vision features of the raw images calculated using the inception v3 model used for image classification. Lower scores indicate the two groups of images are more similar, or have more similar statistics, with a perfect score being 0.0 indicating that the two groups of images are identical.
The FID score is used to evaluate the quality of images generated by generative adversarial networks, and lower scores have been shown to correlate well with higher quality images.
In this tutorial, you will discover how to implement the Frechet Inception Distance for evaluating generated images.
After completing this tutorial, you will know:
- The Frechet Inception Distance summarizes the distance between the Inception feature vectors for real and generated images in the same domain.
- How to calculate the FID score and implement the calculation from scratch in NumPy.
- How to implement the FID score using the Keras deep learning library and calculate it with real
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