Updated README.md for download scripts

modules/dnns_easily_fooled/README.md

@@ -24,6 +24,9 @@ This is an installation process that requires two main software packages (includ
     * 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
@@ -41,7 +44,7 @@ Please see the [Installation_Guide](https://github.com/Evolving-AI-Lab/fooling/w
 * 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 use the `ascent` branch instead of master, because the two required versions of Caffe are different.
+* 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