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name: GoogLeNet_cars

caffemodel: googlenet_finetune_web_car_iter_10000.caffemodel

license: non-commercial

caffe_commit: 737ea5e936821b5c69f9c3952d72693ae5843370

	https://github.com/linnovate/mean-admin/blob/master/server/controllers/users.js
	https://github.com/linnovate/mean-admin/blob/master/public/directives/editable.js

	http://icelab.com.au/articles/levelling-up-with-angularjs-building-a-reusable-click-to-edit-directive/
	http://weblogs.asp.net/dwahlin/creating-custom-angularjs-directives-part-i-the-fundamentals

	https://github.com/linnovate/mean-admin/blob/master/public/views/users.html

	http://weblogs.asp.net/dwahlin/creating-custom-angularjs-directives-part-i-the-fundamentals
	http://weblogs.asp.net/dwahlin/creating-custom-angularjs-directives-part-2-isolate-scope

	other = imread(path2, CV_LOAD_IMAGE_COLOR); cvtColor(other, other, CV_RGB2GRAY); other(ROI).copyTo(other_target);

	namedWindow( "reference", WINDOW_AUTOSIZE );
	//setMouseCallback("reference",on_mouse, NULL ); ;
	imshow( "reference", ref );

	void on_mouse( int event, int x, int y, int d, void *ptr )
	{
	if ( event == EVENT_LBUTTONDOWN )
	{

	#include <DHT.h> // DHT sensor library
	#include <avr/sleep.h> // sleep library
	#include <stdlib.h> // library for maths
	//https://github.com/dicksondickson/PlantFriends/blob/master/sensor_node/sensor_node.ino
	// DHT Humidity + Temperature sensor define
	#define DHTPIN 5 // Data pin (D5) for DHT
	#define DHTPWR 4 // turn DHT on and off via transistor
	#define DHTTYPE DHT11 // sensor model DHT11
	DHT dht(DHTPIN, DHTTYPE); // define DHT11

	/* MQTT/Phant RGB Controller
	* by: Jim Lindblom, SparkFun Electronics
	* with lots of help from: Todd Treece (thanks Todd!)
	*
	* Fun with MQTT, Arduino, and Phant. This example sketch uses
	* MQTT to subscribe to a Phant data stream. When values labeled
	* "red", "green", or "blue" change on the Phant stream, the
	* Arduino will be notified and update its LEDs accordingly.
	*/
	#include <SPI.h> // Include SPI for the Ethernet library

	Part 1: Predicting Movie Ratings
	One of the most common uses of data is to predict what users want. This allows Google to show you relevant ads, Amazon to recommend relevant products, and Netflix to recommend movies that you might like. In this assignment, you'll explore how to recommend movies to a user. We'll start with some basic methods, and then use machine learning to make more sophisticated predictions.

	We'll use Spark for this assignment. In part 1 of the assignment, you'll run Spark on your local machine and on a smaller dataset. The purpose of this part of the assignment is to get everything working before adding the complexities of running on many machines. The interface for running local Spark jobs is exactly the same as the interface for running jobs on a cluster, so you'll be using the same functions we used in lab, and all of the code you write locally can be executed on a cluster. In part 2, which will be released after the midterm, you'll run Spark on a cluster that we have running for you (

	import numpy as np
	import os, sys, getopt

	# Main path to your caffe installation
	caffe_root = '/path/to/your/caffe/'

	# Model prototxt file
	model_prototxt = caffe_root + 'models/bvlc_googlenet/deploy.prototxt'

	# Model caffemodel file