Comments (1)
I agree that NIMA is rather practical, art is subjective even for humans. Interesting insights you have gathered!!
One possible high-level explanation I have for the difference in results - MobileNet and NASNet Mobile are light-weight models while NASNet is not. You can dig into the specifics if you're interested but it's commonly observed that they will behave differently (e.g. YOLO vs YOLO Tiny, although they are obj det models but you get the point!).
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
- A fundamental question! HOT 6
- Theano backend HOT 3
- License?
- any install instructions ? HOT 1
- How to feed the y_labels or scores from the images to model?
- 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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from neural-image-assessment.