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koniq-PyTorch

A PyTorch implementation of No-Reference Image Quality Assessment (NR-IQA) models trained on the KonIQ-10k dataset, proposed in the IEEE TIP paper "KonIQ-10k: An ecologically valid database for deep learning of blind image quality assessment".

The code is based on koncept512_train_test_py3.ipynb provided in the official repository of the paper.

Download the KonIQ-10k dataset with ground truth:

wget "http://datasets.vqa.mmsp-kn.de/archives/koniq10k_512x384.zip"

wget "https://github.com/subpic/koniq/blob/master/metadata/koniq10k_distributions_sets.csv"

unzip koniq10k_512x384.zip

Train/test the optimal koncept512 model in the paper:

-Traing/test code in koncept512_train_test_pytorch.ipynb.

-The pre-trained model is also available.

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