Back-merge documentation and fixes

* master:
  host materials on dl.caffe.berkeleyvision.org
  fix caffe acm-mm paper link
  [docs] readme
  [docs] got rid of redundant README, updated development instructions
  [docs] reworked index page, got rid of publications page
  point to reference performance from installation, add GTX 770
  acknowledge BVLC PI Trevor Darrell for advising Caffe
  add latest CUDA arch to fix invalid device function errors
  switch language to "related publications"
  add publication section to homepage
  fix caffe paper link -- still hasn't appeared on arxiv yet
  Added top-1 and top-5 accuracy for the caffe networks to docs
  add skeleton of the Caffe publications page
  Update docs on building boost on OSX for the python wrappers
  fix OSX 10.9 homebrew CXX doc
  caffe.Net preprocessing members belong to object, not class
  10.9 install doc formatting

diff --cc docs/getting_pretrained_models.md
index a7b5e87,bbac5ac..79b7858
--- 1/docs/getting_pretrained_models.md
--- 2/docs/getting_pretrained_models.md
+++ b/docs/getting_pretrained_models.md
@@@ -23,7 -24,6 +24,8 @@@ This page will be updated as more model
  - The bundled model is the iteration 360,000 snapshot.
  - The best validation performance during training was iteration 358,000 with
    validation accuracy 57.258% and loss 1.83948.
+ - This model obtains a top-1 accuracy 57.1% and a top-5 accuracy 80.2% on the validation set, using just the center crop. (Using the average of 10 crops, (4 + 1 center) * 2 mirror, should obtain a bit higher accuracy)
  
 +**R-CNN (ILSVRC13)**: The pure Caffe instantiation of the [R-CNN](https://github.com/rbgirshick/rcnn) model for ILSVRC13 detection. Download the model (230.8MB) by running `examples/imagenet/get_caffe_rcnn_imagenet_model.sh` from the Caffe root directory. This model was made by transplanting the R-CNN SVM classifiers into a `fc-rcnn` classification layer, provided here as an off-the-shelf Caffe detector. Try the [detection example](http://nbviewer.ipython.org/github/BVLC/caffe/blob/master/examples/detection.ipynb) to see it in action. For the full details, refer to the R-CNN site. *N.B. For research purposes, make use of the official R-CNN package and not this example.*
 +
  Additionally, you will probably eventually need some auxiliary data (mean image, synset list, etc.): run `data/ilsvrc12/get_ilsvrc_aux.sh` from the root directory to obtain it.

diff --cc docs/index.md
Simple merge

diff --cc docs/installation.md
index 1d2c77e,cc2f848..c32f2d0
--- 1/docs/installation.md
--- 2/docs/installation.md
+++ b/docs/installation.md
@@@ -146,9 -144,13 +146,13 @@@ After this, ru
  **Note** that the HDF5 dependency is provided by Anaconda Python in this case.
  If you're not using Anaconda, include `hdf5` in the list above.
  
+ **Note** that in order to build the caffe python wrappers you must install boost using the --with-python option:
+ 
+     brew install --build-from-source --with-python --fresh -vd boost
+ 
  #### Windows
  
 -There is an unofficial Windows port of Caffe at [niuzhiheng/caffe:windows](https://github.com/niuzhiheng/caffe). Thanks [@niuzhiheng](https://github.com/niuzhiheng).
 +There is an unofficial Windows port of Caffe at [niuzhiheng/caffe:windows](https://github.com/niuzhiheng/caffe). Thanks [@niuzhiheng](https://github.com/niuzhiheng)!
  
  ## Compilation