Code for our CVPR 2016 paper on Fashion styles in 128 floats.
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README.md

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
  esimo@aoni.waseda.jp, 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,
}