Fashion Style in 128 Floats

Edgar Simo-Serra, Hiroshi Ishikawa

Overview

This code provides an implementation of the research paper:

  "Fashion Style in 128 Floats: Joint Ranking and Classification using Weak Data for Feature Extraction"
  Edgar Simo-Serra and Hiroshi Ishikawa
  Conference in Computer Vision and Pattern Recognition (CVPR), 2016

See our project page for more detailed information.

License

  Copyright (C) <2016> <Edgar Simo-Serra>

  This work is licensed under the Creative Commons
  Attribution-NonCommercial-ShareAlike 4.0 International License. To view a copy
  of this license, visit http://creativecommons.org/licenses/by-nc-sa/4.0/ or
  send a letter to Creative Commons, PO Box 1866, Mountain View, CA 94042, USA.

  Edgar Simo-Serra, Waseda University
  [email protected], http://hi.cs.waseda.ac.jp/~esimo/

Dependencies

All packages should be part of a standard Torch7 install. For information on how to install Torch7 please see the official torch documentation on the subject.

Usage

Test the model with

th test.lua

You should see a 7x7 matrix displayed on screen which are the descriptor distance values between the seven example images provided in this repository.

Notes

Model provided is the best performing model corresponding to "Ours Joint" in the paper.
This was developed on a linux machine running Ubuntu 14.04 during late 2015.
The provided code does not use GPU accelerated (trivial to change).
Provided model and sample code is under a non-commercial creative commons license.

Dataset

The model was trained on a "clean" subset of the Fashion144k dataset. The dataset used will be released shortly.

Citing

If you use this code please cite:

@InProceedings{SimoSerraCVPR2016,
   author    = {Edgar Simo-Serra and Hiroshi Ishikawa},
   title     = {{Fashion Style in 128 Floats: Joint Ranking and Classification using Weak Data for Feature Extraction}},
   booktitle = "Proceedings of the Conference on Computer Vision and Pattern Recognition (CVPR)",
   year      = 2016,
}

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