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--mirror=1amplifies the dataset with x-flips. Often beneficial, even with ADA. --resume=ffhq1024 --snap=10performs transfer learning from FFHQ trained at 1024x1024. --resume=~/training-runs/<NAME>/network-snapshot-<INT>.pklresumes a previous training run. ...
The first example looks up the training configuration and performs the same operation as if--metrics=pr50k3_fullhad been specified during training. The second example downloads a pre-trained network pickle, in which case the values of--mirrorand--datamust be specified explicitly. ...
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Build requirements: (besides corresponding -devel packages) autoconf-13-1 autoconf-15-1 automake-11-1 binutils-2.36.1-2 cmake-3.20.0-1 cygport-0.34.0-1 gcc-core-10.2.0-1 gcc-fortran-10.2.0-1 gcc-g++-10.2.0-1 libtool-2.4.6-7 make-4.3-1 binutils-2.41-1 cygport-0.36.6-1 gcc-...
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python calc_metrics.py --metrics=fid50k_full --data=~/datasets/ffhq.zip --mirror=1 \ --network=https://nvlabs-fi-cdn.nvidia.com/stylegan2-ada-pytorch/pretrained/ffhq.pklThe first example looks up the training configuration and performs the same operation as if --metrics=pr50k3_full had...
python calc_metrics.py --metrics=fid50k_full --data=~/datasets/ffhq.zip --mirror=1 \ --network=https://nvlabs-fi-cdn.nvidia.com/stylegan2-ada-pytorch/pretrained/ffhq.pklThe first example looks up the training configuration and performs the same operation as if --metrics=pr50k3_full had...