generative-evaluation-prdc
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clovaai
Code base for the precision, recall, density, and coverage metrics for generative models. ICML 2020.
Dear Sir, it seems that in your work there aren't tools for getting the vectors of features from images. Thus, have you got any advice for me to obtain these...
Hello and thank you for this great paper and implementation. I've run your method with a dummy example: ```python fake_features = torch.ones((1024, 4096)) real_features = torch.ones((1024, 4096)) ``` and would...
Thank you for providing the coding and implementation for density and coverage. It is awesome to have the code ready for use in practice. I cite the paper whenever possible....
a simple change, that splits out the density and coverage function from `compute_prdc`. This helps users to omit the CPU cycles required to evaluate precision and recall.
Could you please add a command-line interface with image directory as argument? ex) * [Inception Score](https://github.com/openai/improved-gan/blob/master/inception_score/model.py) * [FID score](https://github.com/mseitzer/pytorch-fid/tree/802da3963113b5b5f8154e0e27580ee4c97460ab) -> `./fid_score.py path/to/dataset1 path/to/dataset2`
Hi! I understand that the density metric is not upper-bounded by 1 and the expectation of density given two identical distributions is 1. However, when I evaluate the density for...
Dear maintainers, the current code is based on numpy, which makes it difficult and slow to use with pytorch tensors. I have implemented the algorithms in pure pytorch (code actually...
