DeokyunKim / Progressive-Face-Super-Resolution

Official Pytorch Implementation of Progressive Face Super-Resolution (BMVC 2019 Accepted)
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Test other LR images #24

Open choieq opened 3 years ago

choieq commented 3 years ago

Hi, Thank you for your work.

I wonder, have you test this model on other LR images? Because when i test my own LR images(LFW), the result was strange.

I convert from HR to LR using your code. image But, unlike CelebA, LFW's resolution is 128x128. So, i remove center crop code.

Result: image

Can you advice me? Thank you.

도움주시면 감사하겠습니다! :ㅇ

DeokyunKim commented 3 years ago

Thank you for your interest in our work. The quality of super-resolved images generated by trained-model relies on train data distribution. i.e., Input low-resolution images has same or similar distribution with train LR-HR pairs(CelebA, LFW).

I think your own dataset has different distribution with our train dataset of coursely. The distribution means noise, blurry, brightness, camera parameters, and etc on your dataset.

In this work, we use downsampling of CelebA and LFW instead of real world LR-HR pairs. Therefore, you should retrain our nerwork with on your own dataset for applying other application or conducting your experiments.

If you have more questions, please let me know. Note that, we can not open overall source code because of company project sequrities.

Thanks!

choieq commented 3 years ago

Thank you for your fast reply!

But above LR examples are in LFW Dataset. Did you also use LFW to train your network? I know that your network only learned from CelebA.

If your network trained by using CelebA, LFW, shouldn't it work in LFW data samples?

DeokyunKim commented 3 years ago

Did you tried to use "checkpoints/unalign_trained_generator_checkpoint.ckpt" ?

choieq commented 3 years ago

if the test data is celeba -> generator_checkpoint_singleGPU.ckpt elif lfw -> unalign_trained_generator_checkpoint.ckpt

Is this right? Can you explain other pre-trained weight? I tried only use generator_checkpoint_singleGPU.ckpt.

It works to use unalign_trained_generator_checkpoint.ckpt in LFW. Thank you very much your reply.. :)

DeokyunKim commented 3 years ago

compressed_model_011000.pth is weights of Compressed Face Alignment Network. generator_checkpoint.ckpt is weights of FSR network trained with aligned dataset (CelebA) and Distributed learning. generator_checkpoint_singleGPU.ckpt is weights of FSR network trained with aligned dataset (CelebA). unalign_trained_generator_checkpoint.ckpt is weights of FSR network trained with unaligned dataset (LFW).