  We investigate the sensitivity of the Fr\'echet Inception Distance (FID)
score to inconsistent and often incorrect implementations across different
image processing libraries. FID score is widely used to evaluate generative
models, but each FID implementation uses a different low-level image processing
process. Image resizing functions in commonly-used deep learning libraries
often introduce aliasing artifacts. We observe that numerous subtle choices
need to be made for FID calculation and a lack of consistencies in these
choices can lead to vastly different FID scores. In particular, we show that
the following choices are significant: (1) selecting what image resizing
library to use, (2) choosing what interpolation kernel to use, (3) what
encoding to use when representing images. We additionally outline numerous
common pitfalls that should be avoided and provide recommendations for
computing the FID score accurately. We provide an easy-to-use optimized
implementation of our proposed recommendations in the accompanying code.
