Comments (4)
I am not sure if we are allowed to share the data that GFLA's authors shared with us. You may want to reach out to GFLA's authors for this. :)
from dressing-in-order.
It seems a training set downsampling/compression problem. when computing FID statistics, GFLA seemingly uses a set of special parameters to preprocess the training/validation set. To reproduce GFLA result and to enable a fair comparison, GFLA's author kindly shared their training set and validation set (with preprocessed downsampling) with us, and that's how we get our numbers for 256x256.
We also get the better set of results (12.25 for us and 9.87 for GFLA) as yours by directly loading the highres images and downsampling by bilinear to compute the FID statistics.
from dressing-in-order.
Thanks for the quick reply. Can you share the code for downsampling the images or the post-processed images?
from dressing-in-order.
Sure, no issues, thanks for the clarifications
from dressing-in-order.
Related Issues (20)
- no images show HOT 1
- Tensors must have same number of dimensions
- img_highres.zip not available HOT 1
- FileNotFoundError: [Errno 2] No such file or directory: 'pretrained_models/flownet.pt' HOT 1
- Thcudacheck fail error and invalid argument while running the demo.py in GFLA HOT 3
- some trouble when running demo
- No such file or directory: 'pretrained_models/flownet.pt' HOT 1
- Not able to get password for img_highres.zip HOT 2
- What is $DATA_DIR in run_eval.sh file if I rerun all the training processing with DFashion dataset ? HOT 1
- Official Colab Released! HOT 4
- Results on custom images HOT 1
- High Resolution
- Typo in repo download file. HOT 1
- Help with inference. HOT 2
- Is there a specific order for the three stages of training? HOT 1
- dataset HOT 1
- NameError: name 'circle' is not defined HOT 4
- Not showing the output images while code is running HOT 1
- While trying custom images my output skeleton tilted -90 degrees HOT 3
- Google colab downloading data problem HOT 2
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from dressing-in-order.