Dataset Licensing

Armory datasets are either licensed or available in accordance to the fair use exception to copyright infringement. The passthrough license is the same as the original license for nonadapted datasets. Adapted datasets ("derivative works") are licensed under the Creative Commons 4.0 International ShareAlike license and are Copyright Two Six Labs, 2020.

Original Licenses

Dataset Original license
MNIST Creative Commons Attribution-Share Alike 3.0
CIFAR-10 MIT
Digit Creative Commons Attribution-ShareAlike 4.0 International
Librispeech Creative Commons 4.0
GTSRB CC0 Public Domain
Imagenette Apache 2.0
UCF101 Fair use exception
RESISC45 Fair use exception
xView Creative Commons Attribution-Noncommercial-ShareAlike 4.0 International
so2sat Creative Commons 4.0
APRICOT Apache License Version 2.0

Attributions

Note: attribution material can be removed upon request to the extent reasonably practicable. Please direct inquiries to armory@twosixlabs.com.

MNIST

Attribution
Creator/author name Yann LeCun and Corinna Cortes
Copyright notice Copyright © 1998 by Yann LeCun and Corinna Cortes
Public license notice http://www.pymvpa.org/datadb/mnist.html
Disclaimer notice UNLESS OTHERWISE MUTUALLY AGREED TO BY THE PARTIES IN WRITING, LICENSOR OFFERS THE WORK AS-IS AND MAKES NO REPRESENTATIONS OR WARRANTIES OF ANY KIND CONCERNING THE WORK, EXPRESS, IMPLIED, STATUTORY OR OTHERWISE, INCLUDING, WITHOUT LIMITATION, WARRANTIES OF TITLE, MERCHANTIBILITY, FITNESS FOR A PARTICULAR PURPOSE, NONINFRINGEMENT, OR THE ABSENCE OF LATENT OR OTHER DEFECTS, ACCURACY, OR THE PRESENCE OF ABSENCE OF ERRORS, WHETHER OR NOT DISCOVERABLE. SOME JURISDICTIONS DO NOT ALLOW THE EXCLUSION OF IMPLIED WARRANTIES, SO SUCH EXCLUSION MAY NOT APPLY TO YOU.
Dataset link http://yann.lecun.com/exdb/mnist/
Modification (Slight) Representation of images as binary tensors
Citation LeCun, Yann, Corinna Cortes, and Christopher JC Burges. "The MNIST database of handwritten digits, 1998." URL http://yann.lecun.com/exdb/mnist 10, no. 34 (1998): 14.

CIFAR-10

Attribution
Creator/attribution parties Alex Krizhevsky, Vinod Nair, and Geoffrey Hinton
Copyright notice Copyright © 2013 by Valay Shah
Public license notice https://peltarion.com/knowledge-center/documentation/terms/dataset-licenses/cifar-10
License text (including disclaimer) Copyright (c) 2013 Valay Shah. Permission is hereby granted, free of charge, to any person obtaining a copy of this software and associated documentation files (the “Software”), to deal in the Software without restriction, including without limitation the rights to use, copy, modify, merge, publish, distribute, sublicense, and/or sell copies of the Software, and to permit persons to whom the Software is furnished to do so, subject to the following conditions: The foregoing copyright notice and this permission notice shall be included in all copies or substantial portions of the Software. THE SOFTWARE IS PROVIDED “AS IS”, WITHOUT WARRANTY OF ANY KIND, EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE.
Dataset link https://www.cs.toronto.edu/~kriz/cifar.html
Citation Krizhevsky, Alex. "Learning Multiple Layers of Features from Tiny Images." URL https://www.cs.toronto.edu/~kriz/learning-features-2009-TR.pdf, (2009).
Modification (Slight) Representation of images as binary tensors

Free Spoken Digit Dataset (FSDD)

Attribution
Creator/attribution parties Zohar Jackson, César Souza, Jason Flaks, Yuxin Pan, Hereman Nicolas, and Adhish Thite
Copyright notice Copyright © 2018 by Zohar Jackson, César Souza, Jason Flaks, Yuxin Pan, Hereman Nicolas, and Adhish Thite
Public license notice https://github.com/Jakobovski/free-spoken-digit-dataset
Disclaimer notice a. Unless otherwise separately undertaken by the Licensor, to the extent possible, the Licensor offers the Licensed Material as-is and as-available, and makes no representations or warranties of any kind concerning the Licensed Material, whether express, implied, statutory, or other. This includes, without limitation, warranties of title, merchantability, fitness for a particular purpose, non-infringement, absence of latent or other defects, accuracy, or the presence or absence of errors, whether or not known or discoverable. Where disclaimers of warranties are not allowed in full or in part, this disclaimer may not apply to You. b. To the extent possible, in no event will the Licensor be liable to You on any legal theory (including, without limitation, negligence) or otherwise for any direct, special, indirect, incidental, consequential, punitive, exemplary, or other losses, costs, expenses, or damages arising out of this Public License or use of the Licensed Material, even if the Licensor has been advised of the possibility of such losses, costs, expenses, or damages. Where a limitation of liability is not allowed in full or in part, this limitation may not apply to You. c. The disclaimer of warranties and limitation of liability provided above shall be interpreted in a manner that, to the extent possible, most closely approximates an absolute disclaimer and waiver of all liability.
Dataset link https://github.com/Jakobovski/free-spoken-digit-dataset
Citation Jackson, Zohar, César Souza, Jason Flaks, Yuxin Pan, Hereman Nicolas, and Adhish Thite. "Jakobovski/free-spoken-digit-dataset: v1.0.8 (Version v1.0.8)." Zenodo (2018). URL http://doi.org/10.5281/zenodo.134240
Modification (Slight) Representation of audio wav file as one-dimensional binary tensors

Librispeech

Attribution
Creator/attribution parties Vassil Panayotov, Guoguo Chen, Daniel Povey and Sanjeev Khudanpur
Copyright notice Copyright © 2014 by Vassil Panayotov
Public license notice http://www.openslr.org/12/
Disclaimer notice a. Unless otherwise separately undertaken by the Licensor, to the extent possible, the Licensor offers the Licensed Material as-is and as-available, and makes no representations or warranties of any kind concerning the Licensed Material, whether express, implied, statutory, or other. This includes, without limitation, warranties of title, merchantability, fitness for a particular purpose, non-infringement, absence of latent or other defects, accuracy, or the presence or absence of errors, whether or not known or discoverable. Where disclaimers of warranties are not allowed in full or in part, this disclaimer may not apply to You. b. To the extent possible, in no event will the Licensor be liable to You on any legal theory (including, without limitation, negligence) or otherwise for any direct, special, indirect, incidental, consequential, punitive, exemplary, or other losses, costs, expenses, or damages arising out of this Public License or use of the Licensed Material, even if the Licensor has been advised of the possibility of such losses, costs, expenses, or damages. Where a limitation of liability is not allowed in full or in part, this limitation may not apply to You. c. The disclaimer of warranties and limitation of liability provided above shall be interpreted in a manner that, to the extent possible, most closely approximates an absolute disclaimer and waiver of all liability.
Dataset link http://www.openslr.org/12/
Citation Panayotov, Vassil, Guoguo Chen, Daniel Povey, and Sanjeev Khudanpur. "Librispeech: an ASR corpus based on public domain audio books." In 2015 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 5206-5210. IEEE, 2015.
Modification (Derivative work) Creation of adversarial dataset that modifies the original audio with small perturbations that are crafted to fool machine learning models.

GTSRB

Attribution
Creator/attribution parties Johannes Stallkamp, Marc Schlipsing, Jan Salmen, and Christian Igel
Copyright notice N/A (public domain)
Public license notice https://www.kaggle.com/meowmeowmeowmeowmeow/gtsrb-german-traffic-sign
Disclaimer notice Affirmer offers the Work as-is and makes no representations or warranties of any kind concerning the Work, express, implied, statutory or otherwise, including without limitation warranties of title, merchantability, fitness for a particular purpose, non infringement, or the absence of latent or other defects, accuracy, or the present or absence of errors, whether or not discoverable, all to the greatest extent permissible under applicable law. Affirmer disclaims responsibility for clearing rights of other persons that may apply to the Work or any use thereof, including without limitation any person's Copyright and Related Rights in the Work. Further, Affirmer disclaims responsibility for obtaining any necessary consents, permissions or other rights required for any use of the Work.
Dataset link http://benchmark.ini.rub.de/?section=gtsrb&subsection=dataset
Citation Stallkamp, Johannes, Marc Schlipsing, Jan Salmen, and Christian Igel. "Man vs. computer: Benchmarking machine learning algorithms for traffic sign recognition." Neural Networks, URL http://www.sciencedirect.com/science/article/pii/S0893608012000457, (2012)
Modification (Derivative work) Creation of adversarial dataset that modifies the original images with small perturbations that are crafted to fool machine learning models.

Imagenette

Attribution
Creator/attribution parties Jeremy Howard
Copyright notice Copyright © 2019 by Jeremy Howard
Public license notice https://github.com/fastai/imagenette
Disclaimer notice Disclaimer of Warranty. Unless required by applicable law or agreed to in writing, Licensor provides the Work (and each Contributor provides its Contributions) on an "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied, including, without limitation, any warranties or conditions of TITLE, NON-INFRINGEMENT, MERCHANTABILITY, or FITNESS FOR A PARTICULAR PURPOSE. You are solely responsible for determining the appropriateness of using or redistributing the Work and assume any risks associated with Your exercise of permissions under this License.
Dataset link https://github.com/fastai/imagenette
Modification (Slight) Representation of images as binary tensors

xView

Attribution
Creator/attribution parties Defense Innovation Unit Experimental (DIUx) and the National Geospatial-Intelligence Agency (NGA)
Copyright notice None found
Public license notice http://xviewdataset.org/terms.html
Disclaimer notice Disclaimer of Warranties and Limitation of Liability. a. Unless otherwise separately undertaken by the Licensor, to the extent possible, the Licensor offers the Licensed Material as-is and as-available, and makes no representations or warranties of any kind concerning the Licensed Material, whether express, implied, statutory, or other. This includes, without limitation, warranties of title, merchantability, fitness for a particular purpose, non-infringement, absence of latent or other defects, accuracy, or the presence or absence of errors, whether or not known or discoverable. Where disclaimers of warranties are not allowed in full or in part, this disclaimer may not apply to You. b. To the extent possible, in no event will the Licensor be liable to You on any legal theory (including, without limitation, negligence) or otherwise for any direct, special, indirect, incidental, consequential, punitive, exemplary, or other losses, costs, expenses, or damages arising out of this Public License or use of the Licensed Material, even if the Licensor has been advised of the possibility of such losses, costs, expenses, or damages. Where a limitation of liability is not allowed in full or in part, this limitation may not apply to You. c. The disclaimer of warranties and limitation of liability provided above shall be interpreted in a manner that, to the extent possible, most closely approximates an absolute disclaimer and waiver of all liability.
Dataset link http://xviewdataset.org/#dataset
Modification (Slight) Representation of images as binary tensors

so2sat

Attribution
Creator/attribution parties Xiaoxiang Zhu, Jingliang Hu, Chunping Qiu, Yilei Shi, Jian Kang, Lichao Mou, Hossein Bagheri, Matthias Haeberle, Yuansheng Hua, Rong Huang, Lloyd Hughes, Hao Li, Yao Sun, Guichen Zhang, Shiyao Han, Michael Schmitt, and Yuanyuan Wang
Copyright notice None found
Public license notice https://mediatum.ub.tum.de/1454690
Disclaimer notice a. UNLESS OTHERWISE SEPARATELY UNDERTAKEN BY THE LICENSOR, TO THE EXTENT POSSIBLE, THE LICENSOR OFFERS THE LICENSED MATERIAL AS-IS AND AS-AVAILABLE, AND MAKES NO REPRESENTATIONS OR WARRANTIES OF ANY KIND CONCERNING THE LICENSED MATERIAL, WHETHER EXPRESS, IMPLIED, STATUTORY, OR OTHER. THIS INCLUDES, WITHOUT LIMITATION, WARRANTIES OF TITLE, MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE, NON-INFRINGEMENT, ABSENCE OF LATENT OR OTHER DEFECTS, ACCURACY, OR THE PRESENCE OR ABSENCE OF ERRORS, WHETHER OR NOT KNOWN OR DISCOVERABLE. WHERE DISCLAIMERS OF WARRANTIES ARE NOT ALLOWED IN FULL OR IN PART, THIS DISCLAIMER MAY NOT APPLY TO YOU. b. TO THE EXTENT POSSIBLE, IN NO EVENT WILL THE LICENSOR BE LIABLE TO YOU ON ANY LEGAL THEORY (INCLUDING, WITHOUT LIMITATION, NEGLIGENCE) OR OTHERWISE FOR ANY DIRECT, SPECIAL, INDIRECT, INCIDENTAL, CONSEQUENTIAL, PUNITIVE, EXEMPLARY, OR OTHER LOSSES, COSTS, EXPENSES, OR DAMAGES ARISING OUT OF THIS PUBLIC LICENSE OR USE OF THE LICENSED MATERIAL, EVEN IF THE LICENSOR HAS BEEN ADVISED OF THE POSSIBILITY OF SUCH LOSSES, COSTS, EXPENSES, OR DAMAGES. WHERE A LIMITATION OF LIABILITY IS NOT ALLOWED IN FULL OR IN PART, THIS LIMITATION MAY NOT APPLY TO YOU. c. The disclaimer of warranties and limitation of liability provided above shall be interpreted in a manner that, to the extent possible, most closely approximates an absolute disclaimer and waiver of all liability.
Dataset link https://mediatum.ub.tum.de/1454690
Modification (Slight) Representation of images as binary tensors

APRICOT

Attribution
Creator/attribution parties A. Braunegg, Amartya Chakraborty, Michael Krumdick, Nicole Lape, Sara Leary, Keith Manville, Elizabeth Merkhofer, Laura Strickhart, and Matthew Walmer
Copyright notice Copyright 2020 APRICOT - MITRE Corporation
Public license notice https://apricot.mitre.org/
Disclaimer notice Disclaimer of Warranty. Unless required by applicable law or agreed to in writing, Licensor provides the Work (and each Contributor provides its Contributions) on an "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied, including, without limitation, any warranties or conditions of TITLE, NON-INFRINGEMENT, MERCHANTABILITY, or FITNESS FOR A PARTICULAR PURPOSE. You are solely responsible for determining the appropriateness of using or redistributing the Work and assume any risks associated with Your exercise of permissions under this License.
Dataset link https://apricot.mitre.org/
Modification (Slight) Representation of images as binary tensors

Fair use notes for RESISC-45 and UCF101

  • Two Six Labs does not charge users for access to the Armory repository, nor the datasets therein, nor does it derive a profit directly from use of the datasets.
  • Two Six Labs is not merely republishing the original datasets. The datasets have undergone transformative changes, specifically they have been repackaged to be integrated with Tensorflow Datasets. This repackaging includes, but is not limited to, processing images from compressed formats into binary tensors as well as decoding audio and video files. Further, Two Six Labs has published derived adversarial datasets that modify the original images/videos with small perturbations that are crafted to fool machine learning models for both the RESISC-45 and UCF101 datasets.
  • Two Six Labs uses these datasets within Armory, however there are other additional datasets present, as well as multiple other features present in Armory beyond providing datasets.
  • Two Six Labs attempted to contact the authors of RESISC-45, but received no response.
  • UCF101 direct download functionality has been used by other machine learning frameworks, such as TensorFlow: https://www.tensorflow.org/datasets/catalog/ucf101
  • Two Six Labs provides public benefit through the public distribution of the Armory framework to evaluate machine learning models. This material is based upon work supported by the Defense Advanced Research Projects Agency (DARPA) under Contract No. HR001120C0114. Any opinions, findings and conclusions or recommendations expressed in this material are those of the author(s) and do not necessarily reflect the views of the Defense Advanced Research Projects Agency (DARPA).

Citations for RESISC45 and UCF101

Cheng, Gong, Junwei Han, and Xiaoqiang Lu. "Remote sensing image scene classification: Benchmark and state of the art." Proceedings of the IEEE 105, no. 10 (2017): 1865-1883.

Soomro, Khurram, Amir Roshan Zamir, and Mubarak Shah. "UCF101: A dataset of 101 human actions classes from videos in the wild." arXiv preprint arXiv:1212.0402 (2012).