Comments (6)
As you say, the implementation is strictly mirrored from the paper, if you take a closer look you will see that the implementation is correct.
from neural-image-assessment.
Sorry for the mistake. I realized it's right. Thanks!
Another minor problem I found is Dropout. Due to pytorch features, you need to explicit call model.eval() in valuation and test period to turn the Dropout into right mode to get right forward result.
from neural-image-assessment.
Fixed the dropout issue
I didn't see any typo that you mentioned
from neural-image-assessment.
可能是我看错了……应该没有typo……
from neural-image-assessment.
Also I see you might also need to add model.eval() at valuation period
from neural-image-assessment.
The EMD loss still seems to be wrong, my opinion is the sum operation should be inside of torch.abs
from neural-image-assessment.
Related Issues (20)
- hi, my training process looks OK, the final loss is about 0.0988. but the result seems random HOT 1
- fully connected layers HOT 2
- About AVA dataset HOT 8
- Some mistake in main.py
- It requires me to install so much packages
- No such file or directory - Please Help HOT 6
- The torchvision pretrained VGG-16 requires normalization of inputs and you do not do this HOT 3
- 你好,我没有训练模型的硬件条件,但是还是想用图像美学。可以分享一下,训练好的权重文件吗??? HOT 1
- how to test myself photo? HOT 2
- Is there pretrained weight for InceptionNet?
- The score for the same picture varies HOT 5
- the google drive link for pretrained model is invalid HOT 8
- The link to the pre-trained model is not working HOT 9
- About the test.py
- csv文件
- AVA dataset download
- test_labels.csv HOT 16
- userwaring: possibly corrupt EXIF data
- Pre-trained model link not working HOT 1
- who can share the pre-train model? HOT 2
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