From 36190960e0b8e31aa470e76132b06a50dcd381a8 Mon Sep 17 00:00:00 2001 From: Anh Nguyen Date: Sun, 29 Nov 2015 14:28:19 -0700 Subject: [PATCH] Trying to fix whitespace_contrib error in build --- modules/dnns_easily_fooled/README.md | 48 ---------------------------- 1 file changed, 48 deletions(-) diff --git a/modules/dnns_easily_fooled/README.md b/modules/dnns_easily_fooled/README.md index 79e9637e2..875cc144a 100644 --- a/modules/dnns_easily_fooled/README.md +++ b/modules/dnns_easily_fooled/README.md @@ -1,51 +1,3 @@ # Fooling Code - This is the code base used to reproduce the "fooling" images in the paper: - [Nguyen A](http://anhnguyen.me), [Yosinski J](http://yosinski.com/), [Clune J](http://jeffclune.com). ["Deep Neural Networks are Easily Fooled: High Confidence Predictions for Unrecognizable Images"](http://arxiv.org/abs/1412.1897). In Computer Vision and Pattern Recognition (CVPR '15), IEEE, 2015. - -**If you use this software in an academic article, please cite:** - - @inproceedings{nguyen2015deep, - title={Deep Neural Networks are Easily Fooled: High Confidence Predictions for Unrecognizable Images}, - author={Nguyen, Anh and Yosinski, Jason and Clune, Jeff}, - booktitle={Computer Vision and Pattern Recognition (CVPR), 2015 IEEE Conference on}, - year={2015}, - organization={IEEE} - } - -For more information regarding the paper, please visit www.evolvingai.org/fooling - -## Requirements -This is an installation process that requires two main software packages (included in this package): - -1. Caffe: http://caffe.berkeleyvision.org - * Our libraries installed to work with Caffe - * Cuda 6.0 - * Boost 1.52 - * g++ 4.6 - * Use the provided scripts to download the correct version of Caffe for your experiments. - * `./download_caffe_evolutionary_algorithm.sh` Caffe version for EA experiments - * `./download_caffe_gradient_ascent.sh` Caffe version for gradient ascent experiments -2. Sferes: https://github.com/jbmouret/sferes2 - * Our libraries installed to work with Sferes - * OpenCV 2.4.10 - * Boost 1.52 - * g++ 4.9 (a C++ compiler compatible with C++11 standard) - -Note: These are specific versions of the two frameworks with our additional work necessary to produce the images as in the paper. They are not the same as their master branches. - -## Installation - -Please see the [Installation_Guide](https://github.com/Evolving-AI-Lab/fooling/wiki/Installation-Guide) for more details. - -## Usage - -* An MNIST experiment (Fig. 4, 5 in the paper) can be run directly on a local machine (4-core) within a reasonable amount of time (around ~5 minutes or less for 200 generations). -* An ImageNet experiment needs to be run on a cluster environment. It took us ~4 days x 128 cores to run 5000 generations and produce 1000 images (Fig. 8 in the paper). -* [How to configure an experiment to test the evolutionary framework quickly](https://github.com/Evolving-AI-Lab/fooling/wiki/How-to-test-the-evolutionary-framework-quickly) -* To reproduce the gradient ascent fooling images (Figures 13, S3, S4, S5, S6, and S7 from the paper), see the [documentation in the caffe/ascent directory](https://github.com/anguyen8/opencv_contrib/tree/master/modules/dnns_easily_fooled/caffe/ascent). You'll need to download the correct Caffe version for this experiment using `./download_caffe_gradient_ascent.sh` script. - -## Updates - -* A fork project [here](https://github.com/Evolving-AI-Lab/innovation-engine) has support for the **latest Caffe** and experiments to create *recognizable* images instead of unrecognizable.