A fundamental question!

titu1994 / neural-image-assessment

Implementation of NIMA: Neural Image Assessment in Keras

MIT License

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A fundamental question! #59

Open arianaa30 opened 5 years ago

arianaa30 commented 5 years ago

I just downloaded your latest release package. And simply running evaluate_mobilenet.py on these two images (replaced img_path = 'images/girl1.jpg'):

While there is a huge difference visually, the good photo's score is even lower! Am I missing any configurations?!

Evaluating :  images/girl1.jpg
NIMA Score : 4.269 +- (2.027)
Evaluating :  images/ugly.jpg
NIMA Score : 4.317 +- (1.792)

girl1 ugly

mr-Mojo commented 5 years ago

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?

img8_bri_0

guantinglin commented 4 years ago

Hi, mr-Mojo and arianaa30 Have you figure out where the problem is?

mr-Mojo commented 4 years ago

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.

arianaa30 commented 4 years ago

NIMA also provides a technical score for things like brightness, contrast, and PSNR-like quality of images. That's a different one from Aescetics.

h3clikejava commented 3 years ago

@arianaa30 Have u figure out?

arianaa30 commented 3 years ago

No I didn't dig more into this after that. What I did figure out was it gives higher rates to faces and natural images in general. Maybe not really darkness/brightness. I didn't test the latter.

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