NIMA: Neural Image Assessment

Implementation of NIMA: Neural Image Assessment in Keras + Tensorflow with weights for MobileNet model trained on AVA dataset.

NIMA assigns a Mean + Standard Deviation score to images, and can be used as a tool to automatically inspect quality of images or as a loss function to further improve the quality of generated images.

Contains weights trained on the AVA dataset for the following models:

• NASNet Mobile (0.067 EMD on valset thanks to @tfriedel !, 0.0848 EMD with just pre-training)
• Inception ResNet v2 (~ 0.07 EMD on valset, thanks to @tfriedel !)
• MobileNet (0.0804 EMD on valset)

Usage

Evaluation

There are evaluate_*.py scripts which can be used to evaluate an image using a specific model. The weights for the specific model must be downloaded from the Releases Tab and placed in the weights directory.

Supports either passing a directory using --dir or a set of full paths of specific images using --img (seperate multiple image paths using spaces between them)

There's also a script sort_lightroom_collection.py, which will sort a Adobe Lightroom collection using the calculated scores. For an example see the screenshots.

Arguments:

--dir    : Pass the relative/full path of a directory containing a set of images. Only png, jpg and jpeg images will be scored.
--img    : Pass one or more relative/full paths of images to score them. Can support all image types supported by PIL.

Training

The AVA dataset is required for training these models. I used about 240.000 images to train and the last 10.000 images to evaluate (this is not the same format as in the paper). You can download it as a torrent from here: http://academictorrents.com/details/71631f83b11d3d79d8f84efe0a7e12f0ac001460

First, ensure that the dataset is clean - no corrupted JPG files etc by using the check_dataset.py script in the utils folder. If such currupted images exist, it will drastically slow down training since the Tensorflow Dataset buffers will constantly flush and reload on each occurance of a corrupted image. Then just run train_nasnet_mobile.py.

Example

best ranked images

worst ranked images

Requirements

• Keras
• Tensorflow (CPU to evaluate, GPU to train)
• path.py
• tqdm
• pillow