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Copyright © 2013 New York University.
All Rights Reserved. A license to use and copy this software and its documentation solely for your internal
research and evaluation purposes, without fee and without a signed licensing agreement, is hereby granted
upon your download of the software, through which you agree to the following:
1) the above copyright notice, this paragraph and the following three paragraphs will prominently appear
in all internal copies and modifications;
2) no rights to sublicense or further distribute this software are granted;
3) no rights to modify this software are granted; and
4) no rights to assign this license are granted.
Please Contact The Office of Industrial Liaison,
New York University, One Park Avenue, 6th Floor, New York, NY 10016 (212) 263-8178,
for commercial licensing opportunities, or for further distribution, modification or license rights.
Created by Pierre Sermanet, Michael Mathieu, and Yann LeCun. http://cilvr.cs.nyu.edu
IN NO EVENT SHALL NYU, OR ITS EMPLOYEES, OFFICERS, AGENTS OR TRUSTEES (“COLLECTIVELY “NYU PARTIES”) BE LIABLE
TO ANY PARTY FOR DIRECT, INDIRECT, SPECIAL, INCIDENTAL, OR CONSEQUENTIAL DAMAGES OF ANY KIND , INCLUDING LOST
PROFITS, ARISING OUT OF ANY CLAIM RESULTING FROM YOUR USE OF THIS SOFTWARE AND ITS DOCUMENTATION, EVEN IF ANY
OF NYU PARTIES HAS BEEN ADVISED OF THE POSSIBILITY OF SUCH CLAIM OR DAMAGE.
NYU SPECIFICALLY DISCLAIMS ANY WARRANTIES OF ANY KIND REGARDING THE SOFTWARE, INCLUDING, BUT NOT LIMITED TO,
NON-INFRINGEMENT, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE, OR THE
ACCURACY OR USEFULNESS, OR COMPLETENESS OF THE SOFTWARE. THE SOFTWARE AND ACCOMPANYING DOCUMENTATION, IF ANY,
PROVIDED HEREUNDER IS PROVIDED COMPLETELY "AS IS". REGENTS HAS NO OBLIGATION TO PROVIDE FURTHER DOCUMENTATION,
MAINTENANCE, SUPPORT, UPDATES, ENHANCEMENTS, OR MODIFICATIONS.
Please cite the paper below if you use OverFeat in your research.
Pierre Sermanet, David Eigen, Xiang Zhang, Michael Mathieu, Rob Fergus, Yann LeCun:
"OverFeat: Integrated Recognition, Localization and Detection using Convolutional Networks",
ArXiv:1312.6229. http://arxiv.org/abs/1312.6229