Comments (2)
Hi, I think that it is up to you because you can use any feature extractors for utilizing this metric.
Commonly, people use InceptionV3(Tensorflow or Pytorch pretrained model) or Vgg16 ImageNet pretrained classifiers for extracting feature vectors and using perceptual evaluation metrics. Although these authors used vgg in their paper, I have used InceptionV3 and this metric has performed well either. I think that people usually extract features from (2048dim) averge pooling layer of InceptionV3 or (4096 dim) second fully connected layer of VGG16.
You would better load your real and fake images, and then preprocess for extracting feature vectors from these classifiers. You can also manage to obtain some reference source code line such as FID about getting feature vectors.
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Related Issues (9)
- FID-infinite comparison and discussion HOT 2
- Density much larger than 1
- [Feature Request] Command line interface HOT 1
- When the number of real samples is smaller than 10K, does the metric still produce a reliable score? HOT 5
- using exact similarity search HOT 1
- How do I use my own image dataset to run your code? HOT 2
- Dummy example gives non intuitive result
- About a specific random embedding extraction method HOT 1
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