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Repo to accompanying the paper: "Predicting trends in the quality of state-of-the-art neural networks without access to training or testing data"

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Repo to accompany Paper

"Predicting trends in the quality of state-of-the-art neural networks without access to training or testing data"

DOI

Requirements to reproduce results:

Python 3.7.6 weightwatcher 0.2.7 (or ww 0.4 with ww2x, and min_size = 50)

Conda environment in requirements.txt

Includes

Jupyter Notebooks for reproducing most Tables and all Figures

All results can be generated using pretrained models available in the torchvision pyTorch models (except ResNet-1K, which requies the Cv Sandbox)

data

Contains data from weightwatcher runs using Google Colab All Tables and Figures are generated directly from this raw data

distiller/

Jupyter Notebooks for reproducing Figure 4 and accompanying text (note: user must install Intell distiller to run these)

submission

original Latex files

img/

images, generated by Jupyter Notebooks

Comments on Reproducibility

The original weightwatcher calculations were done in the Summer of 2019, and then repeated in Jan 2020 using more pretrained models (from the OSMR repo)

Since that time, the weightwatcher code has been updated, and the OSMR models have have changed

This paper reports details results from the Jan 2020 data, stored in data/omsr

The calculations can be repeated using weightwatcher (with ww2x=True set) however, there may be minor differences in the numerical results.

Deprecated: ww-colab/

Data from older submission: Google Colab Notebooks for reproducing results in sections 6

Notebooks can be run in parallel on the users Google Cloub They Will download pretrained models from the CV Sandbox

MIT License

Copyright (c) [year] [fullname]

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 above 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.

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Repo to accompanying the paper: "Predicting trends in the quality of state-of-the-art neural networks without access to training or testing data"

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