Comments (4)
I removed that line in the latest commit, but the scores do not change from the previous discussion
Evaluating : images/art1.jpg
NIMA Score : 5.988 +- (1.575)
Evaluating : images/art2.jpg
NIMA Score : 4.268 +- (1.694)
Evaluating : images/art3.jpg
NIMA Score : 4.655 +- (1.521)
Evaluating : images/art4.jpg
NIMA Score : 5.558 +- (1.510)
Evaluating : images/art5.jpg
NIMA Score : 6.123 +- (1.574)
Evaluating : images/art6.jpg
NIMA Score : 6.596 +- (1.607)
This is after resizing images to 224x224 directly. I will try your resize script from the drive folder to see if it makes much of a difference
from neural-image-assessment.
With the resize script from your drive, these are the following scores
Evaluating : images/art1.jpg
NIMA Score : 5.710 +- (1.470)
Evaluating : images/art2.jpg
NIMA Score : 5.623 +- (1.440)
Evaluating : images/art3.jpg
NIMA Score : 5.365 +- (1.346)
Evaluating : images/art4.jpg
NIMA Score : 5.591 +- (1.512)
Evaluating : images/art5.jpg
NIMA Score : 6.244 +- (1.549)
Evaluating : images/art6.jpg
NIMA Score : 6.037 +- (1.561)
These scores are even more scattershot than before. Obviously, the art pieces are very highly influenced by their aspect ratio when resizing, cropping or any other operation, causing large fluctuations in their scores.
from neural-image-assessment.
You need to choose some images which have more similarity to the training set.
I did write a script which sorts images in lightroom according to the scores and used in on some personal folders, holiday images etc.
It did an okay job, but not really good enough in my opinion. Okay the images with bad scores were always bad, like blurry or accidental photos. Some of the highest rated images were kind of abstract and had repetitive elements.
I had one folder with lots of random images, like even screenshots from google maps for example. And they were rated rather highly ^^
But no wonder, those kind of images were not in the training so it had no idea what to do with them.
I guess this is a problem when you only consider images which were submitted to photo contests. A better training set in this case would include typical images people have on their phones.
from neural-image-assessment.
That seems to be the case.
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?
- 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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from neural-image-assessment.