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,
}