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Pytorch code for our work "Representation Learning of Image Composition for Aesthetic Evaluation".


Lin Zhao, Meimei Shang, Fei Gao*, et al. Representation Learning of Image Composition for Aesthetic Prediction. Computer Vision and Image Understanding (CVIU), vol. 199, 103024, Oct. 2020. [paper]




  • pytorch
  • torchvision
  • tqdm
  • requests

Code (folder)

  • It contains AVA, CPC, JAS_composition, JAS_aesthetic.
    • AVA: aesthetic prediction on the AVA dataset;
    • CPC: composition prediciotn on the CPC dataset;
    • JAS_composition: composition prediction on the JAS dataset;
    • JAS_aesthetic: aesthetic prediction on the JAS dataset;
  • Pretrained models are released in pretrain_model
    • e denotes ReLIC_e
    • u denotes ReLIC_u
    • ReLIC denotes ReLIC
    • ReLIC1 denotes ReLIC+
    • ReLIC2 denotes ReLIC++
  • you can change the 'path_to_model_weight' in and run start_check_model in
  • if you want to train your own models, please run start_train in

Feel free to ask any questions about coding.

Data (folder)




title = "Representation learning of image composition for aesthetic prediction",
author = "Lin Zhao and Meimei Shang and Fei Gao and Rongsheng Li and Fei Huang and Jun Yu",
journal = "Computer Vision and Image Understanding",
volume = "199",
pages = "103024",
year = "2020",
issn = "1077-3142",
doi = "",

References (selected)

  • Amirshahi, S.A., Hayn-Leichsenring, G.U., Denzler, J., Redies, C., 2014a. Jenaaesthetics dataset URL:
  • Amirshahi, S.A., Hayn-Leichsenring, G.U., Denzler, J., Redies, C., 2014b. Jenaesthetics subjective dataset: analyzing paintings by subjective scores, in: European Conference on Computer Vision, Springer. pp. 3–19.
  • Deng, Y., Chen, C.L., Tang, X., 2017. Image aesthetic assessment: An experimental survey. IEEE Signal Processing Magazine 34, 80–106.
  • Murray, N., Marchesotti, L., Perronnin, F., 2012. AVA: A large-scale database for aesthetic visual analysis, in: IEEE International Conference on Computer Vision and Pattern Recognition, pp. 2408–2415.
  • Wei, Z., Zhang, J., Shen, X., Lin, Z., Mech, R., Hoai, M., Samaras, D., 2018. Good view hunting: Learning photo composition from dense view pairs, in: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 5437–5446.
  • Ma, S., Liu, J., Wen Chen, C., 2017. A-lamp: Adaptive layout-aware multipatch deep convolutional neural network for photo aesthetic assessment , 4535–4544.
  • Talebi, H., Milanfar, P., 2018. NIMA: Neural image assessment. IEEE Transactions on Image Processing 27, 3998–4011. doi:10.1109/TIP.2018.2831899.