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A deep ranking network that learns to find good compositions in a photograph.
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View Finding Network

This repository contains the dataset and scripts used in the following article:

Yi-Ling Chen, Jan Klopp, Min Sun, Shao-Yi Chien, Kwan-Liu Ma, "Learning to Compose with Professional Photographs on the Web", in Proc. of ACM Multimedia 2017. (Supplemetnal)

News We are reimplementing this method in PyTorch. Check back often if you are interested.


You will need to have tensorflow (version > 1.0), skimage, tabulate, pillow installed on your system to run the scripts.

Download the dataset

  • Clone the repository to your local disk.
  • Under a command line window, run the following command to get the training images from Flickr:
$ python -w 4

The above command will launch 4 worker threads to download the images to a default folder (./images).


  • Run to generate the TFRecords files used by Tensorflow.
  • Run to start training.
$ python --spp 0

The above example starts training with SPP disabled. Or you may want to enable SPP with either max or avg options.

$ python --pooling max

Note that if you changed the output filenames when running, you will need to provide the new filenames to Take a look at the script to check out other available parameters or run the following command.

$ python -h


We provide the evaluation script to reproduce our evaluation results on Flickr cropping dataset. For example,

$ python --spp false --snapshot snapshots/model-wo-spp

You will need to get sliding_window.json and the test images from the Flickr cropping dataset and specify the path of your model when running You can also try our pre-trained model, which can be downloaded from here.

If you want to get an aesthetic score of a patch, please take a look at the example featured by ModelDepot


If you have questions/suggestions, feel free to send an email to (yiling dot chen dot ntu at gmail dot com).

If this work helps your research, please cite the following article:

  title={Learning to Compose with Professional Photographs on the Web},
  author={Yi-Ling Chen and Jan Klopp and Min Sun and Shao-Yi Chien and Kwan-Liu Ma},
  booktitle={ACM Multimedia 2017},
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