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KonIQ-10k Deep Learning Models
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README.md
resnet101.py
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train_koncept1024.ipynb
train_koncept224.ipynb
train_koncept512.ipynb

README.md

KonIQ-10k models

Deep Learning Models for the KonIQ-10k Image Quality Assessment Database

This is part of the code for the paper "KonIQ-10k: An ecologically valid database for deep learning of blind image quality assessment". The included notebooks rely on the kutils library. Project data is available for download from osf.io. Please cite the following paper if you use the code:

@misc{koniq10k,
    title={KonIQ-10k: An ecologically valid database for deep learning of blind image quality assessment},
    author={Hosu, Vlad and Lin, Hanhe and Sziranyi, Tamas and Saupe, Dietmar},
    year={2019},
    eprint={1910.06180},
    archivePrefix={arXiv},
    primaryClass={cs.CV}
}

Overview

Python 2.7 notebooks:

train_koncept512.ipynb, train_koncept224.ipynb:

  • Training and testing code for the KonCept512 and KonCept224 model (on KonIQ-10k).
  • Ready-trained model weights for KonCept512 and KonCept224.

train_deeprn.ipynb

  • Reimplementation of the DeepRN model trained on KonIQ-10k, following the advice of the original author, Domonkos Varga.
  • Re-trained model weights (on SPP features) are available here.
  • The features extracted from KonIQ-10k are available here.

metadata/koniq10k_distributions_sets.csv

  • Contains image file names, scores, and train/validation/test split assignment (random).
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