Comments (6)
I have the same problem, the following images receive ratings that do not make any sense. The bright, beautiful image is score with 6.485, and the dark version which is clearly inferior receives a 6.669. Any thoughts on why this might be?
from neural-image-assessment.
Hi, mr-Mojo and arianaa30
Have you figure out where the problem is?
from neural-image-assessment.
Hi @guantinglin, I think that NIMA might be insensitive to lightning and brightness change in images. I confirmed this observation by testing with different implementations from GitHub and my own research. The difference in prediction score is probably due to changes in contrast etc., that come with brightness changes.
from neural-image-assessment.
NIMA also provides a technical score for things like brightness, contrast, and PSNR-like quality of images. That's a different one from Aescetics.
from neural-image-assessment.
@arianaa30 Have u figure out?
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from neural-image-assessment.
Related Issues (20)
- when i train model on TID2013 dataset,something went wrong HOT 8
- Weight freezing in train*.py HOT 2
- I've computed histograms of the ground truth and predicted scores HOT 5
- Can't instantiate abstract class DatasetV1 with abstract methods _as_variant_tensor, _inputs HOT 5
- Regarding results
- How to set arguments: HOT 1
- Tensorflow version Issue HOT 1
- Different results
- errors when i run extract_inception_features.py HOT 7
- parse data function-image augmentation HOT 4
- Theano backend HOT 3
- License?
- any install instructions ? HOT 1
- How to feed the y_labels or scores from the images to model?
- Van Gogh's Mona Lisa is only 4.266 points??? HOT 1
- Nasnet weights? HOT 1
- Performance issues in the program HOT 2
- Performance issues in utils/data_loader.py (by P3) HOT 1
- Dataset format HOT 1
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