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", to appear in ACM Multimedia 2017.

Dependencies

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 download_images.py -w 4

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

Training

• Run create_dbs.py to generate the TFRecords files used by Tensorflow.
• Run vfn_train.py to start training.

$ python vfn_train.py --spp 0

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

$ python vfn_train.py --pooling max

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

$ python vfn_train.py -h

Evaluation

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

$ python vfn_eval.py --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 vfn_eval.py. You can also try our pre-trained model, which can be downloaded from here.

Questions?

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:

@inproceedings{chen-acmmm-2017,
  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},
  year={2017}
}