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HOWTO Cross compiling on Android

5W1H

What is NDK

NDK (Native Develop Toolkit) is a toolchain from Android official, originally for users who writes native C/C++ code as JNI library. It's not designed for compiling standalone programs (./a.out) and not compatible with automake/cmake etc.

What is Standalone Toolchain

"Standalone" refers to two meanings:

1. The program is standalone (has nothing connect to NDK, and don't need helper scripts to run it)
2. The toolchain is made for building standalone programs and libs, and which can used by automake etc.

(Optional) Why NDK is hard to use

By default, NDK uses android flavor directory structure when it's finding headers and libs, which is different from GNU flavor, so the compiler cannot find them. For Example:

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Gil Levi and Tal Hassner, Emotion Recognition in the Wild via Convolutional Neural Networks and Mapped Binary Patterns

Convolutional neural networks for emotion classification from facial images as described in the following work:

Gil Levi and Tal Hassner, Emotion Recognition in the Wild via Convolutional Neural Networks and Mapped Binary Patterns, Proc. ACM International Conference on Multimodal Interaction (ICMI), Seattle, Nov. 2015

Project page: http://www.openu.ac.il/home/hassner/projects/cnn_emotions/

If you find our models useful, please add suitable reference to our paper in your work.

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Age and Gender Classification using Convolutional Neural Networks

###name: Age Classification CNN

caffemodel: age_net.caffemodel

caffemodel_url: https://dl.dropboxusercontent.com/u/38822310/age_net.caffemodel

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name	caffemodel	caffemodel_url	license	sha1	caffe_commit
Vanilla CNN Model	vanillaCNN.caffemodel	https://github.com/ishay2b/VanillaCNN/blob/master/ZOO/vanillaCNN.caffemodel?raw=true	unrestricted	b5e34ce75d078025e07452cb47e65d198fe27912	9c9f94e18a8909580a6b94c44dbb1e46f0ee8eb8

Implementation of the Vanilla CNN described in the paper: Yue Wu and Tal Hassner, "Facial Landmark Detection with Tweaked Convolutional Neural Networks", arXiv preprint arXiv:1511.04031, 12 Nov. 2015. See project page for more information about this project.

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name: CaffeNet fine-tuned on the Oxford 102 category flower dataset

caffemodel: oxford102.caffemodel

caffemodel_url: https://s3.amazonaws.com/jgoode/oxford102.caffemodel

gist_id: 0179e52305ca768a601f

license: non-commercial

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Instructions for making a Neural-Style movie

The following instructions are for creating your own animations using the style transfer technique described by Gatys, Ecker, and Bethge, and implemented by Justin Johnson. To see an example of such an animation, see this video of Alice in Wonderland re-styled by 17 paintings.

Setting up the environment

The easiest way to set up the environment is to simply load Samim's a pre-built Terminal.com snap or use another cloud service like Amazon EC2. Unfortunately the g2.2xlarge GPU instances cost $0.99 per hour, and depending on parameters selected, it may take 10-15 minutes to produce a 512px-wide image, so it can cost $2-3 to generate 1 sec of video at 12fps.

If you do load the

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Deep Learning of Binary Hash Codes for Fast Image Retrieval

name: Deep Binary Hash Codes CIFAR10

caffemodel: KevinNet_CIFAR10_48.caffemodel

caffemodel_url: https://www.dropbox.com/s/1om7xa8mz93wkzh/KevinNet_CIFAR10_48.caffemodel?d

gist_id: 266d4150a1db5810398e

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# The network is used for the video description experiments in [1].
# Please consider citing [1] if you use this example in your work.
#
# [1] S. Venugopalan, H. Xu, J. Donahue, M. Rohrbach, R. Mooney, K.Saenko.
#     "Translating Videos to Natural Language using Deep Recurrrent Neural
#     Networks." NAACL-HLT 2015.

name: "mean_fc7_to_lstm"
layer {
  name: "data"

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##Sequence to Sequence -- Video to Text

Paper : [ICCV 2015 PDF](http://www.cs.utexas.edu/users/ml/papers/venugopalan.iccv15.pdf)

Download Model: [S2VT_VGG_RGB_MODEL](https://www.dropbox.com/s/wn6k2oqurxzt6e2/s2s_vgg_pstream_allvocab_fac2_iter_16000.caffemodel?dl=1) (333MB)

[Project Page](https://vsubhashini.github.io/s2vt.html)

### Description

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Multiple SSH Keys settings for different github account

create different public key

create different ssh key according the article Mac Set-Up Git

$ ssh-keygen -t rsa -C "your_email@youremail.com"