haomingcai / pipal-dataset Goto Github PK
View Code? Open in Web Editor NEW[ Official ] - PIPAL Dataset and Training Codebase. ECCV-2020, NTIRE-21/22.
Home Page: https://www.jasongt.com/projectpages/pipal.html
[ Official ] - PIPAL Dataset and Training Codebase. ECCV-2020, NTIRE-21/22.
Home Page: https://www.jasongt.com/projectpages/pipal.html
Hello,
I want to rank the qualities of some images, and I wonder is there any plan for releasing the pretrained model? Thanks!
I encountered an error "NotImplementedError: Check Alignment" when submitting my test results for PIPAL-NTIRE22 challenge. Could you please help look into what might be causing this issue?
Hello, can you provide the distortion type names corresponding to all the images?
If
A0001_01_01.bmp represents, what is the distortion type
What a distortion type does the A0001_01_02.bmp represent
thank you very much
Hello, when I was testing your code, I found that you did not give the label data of VAL and test set, so I can’t run the testIQA.yml, how can this solve this?
The error is “FileNotFoundError: [Errno 2] No such file or directory: '/home/hmcai/home/Dataset/PIPAL/PIPAL_Partition/FullDataset/Full_EloMOSV2'”
Hi, is it possible to release the code for generating distorted images? This code may be important for further IQA research, such as generating more distorted images for data augmentation.
Hi, could you provide the MOS data ?
Thank you, I am looking forward for your reply
It's been a while since you mentioned in the README that the test set is coming soon.
Hi, is it possible to release the code for generating distorted images? This code may be important for further IQA research, such as generating more distorted images for data augmentation.
Hello, thanks for your great job! Where can we find the IQOS to collect our own data? I cannot find the usage of IQOS on the github/project web page.
Hi, is it possible to release the code for generating distorted images? This code may be important for further IQA research, such as generating more distorted images for data augmentation.
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