OpenCV3.4.0 vs2015 win64 + cmake编译生成的带opencv_contrib-3.4.0库文件（含...

# 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) * Use the provided script `./download_sferes.sh` to download the correct version of Sferes. Note: These are patched 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/anguyen8/opencv_contrib/blob/master/modules/dnns_easily_fooled/Installation_Guide.pdf) 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. ## Troubleshooting 1. If Sferes (Waf) can't find your CUDA and Caffe dynamic libraries > Add obj.libpath to the wscript for exp/images to find libcudart and libcaffe or you can use LD_LIBRARY_PATH (for Linux). 2. Is there a way to monitor the progress of the experiments? > There is a flag for printing out results (fitness + images) every N generations. You can adjust the dump_period setting [here](https://github.com/Evolving-AI-Lab/fooling/blob/master/sferes/exp/images/dl_map_elites_images.cpp#L159). 3. Where do I get the pre-trained Caffe models? > For AlexNet, please download on Caffe's Model Zoo. > For LeNet, you can grab it [here](https://github.com/anguyen8/opencv_contrib/tree/master/modules/dnns_easily_fooled/model/lenet). 4. How do I run the experiments on my local machine without MPI? > You can enable MPI or non-MPI mode by commenting/uncommenting a line [here](https://github.com/Evolving-AI-Lab/fooling/blob/master/sferes/exp/images/dl_map_elites_images_mnist.cpp#L190-L191). It can be simple eval::Eval (single-core), eval::Mpi (distributed for clusters).