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This paper proposes a Fast Region-based Convolutional Network method (Fast R-CNN) for object detection. Fast R-CNN builds on previous work to efficiently classify object proposals using deep convolutional networks. Compared to previous work, Fast R-CNN employs several innovations to improve training and testing speed while also increasing detection accuracy. Fast R-CNN trains the very deep VGG16 network 9× faster than R-CNN, is 213× faster at test-time, and achieves a higher mAP on PASCAL VOC 2012. Compared to SPPnet, Fast R-CNN trains VGG16 3× faster, tests 10× faster, and is more accurate.

当你在写react的时候报了类似于这样子的错： Each child in an array or iterator should have a unique “key” prop. 原因是这样子的： React can’t know that your array is static, so you get the warning. The most practical thing to do here is to write something like. 解决办法只要在循环的每个子项添加一个key就行了，代码如下： var names = [‘Alice’, ‘Emily’, ‘Kate’]; ReactDOM.render( <div> { names.map(function (name, key) { return <div key={key}>Hello, {name}!</div> }) } </div>, document.getElementById(‘example’) ); 来源： http://www.cnblogs.com/jh1994/p/5334128.html

Each child in an array or iterator should have a unique "key" prop. 解决办法使用React.Children.toArray import React from 'react' import Slider from 'react-slick' export default class Table extends React.Component{ constructor(props) { super(props) this.state = { th: [ "名称", "城市", "邮编" ] } } render() { const { table, children } = this.props return( <table className="table table-striped"> <caption>条纹表格布局</caption> <thead> <tr> { React.Children.map( this.state.th , th => <th>{ th }</th> ) } </tr> </thead> <tbody> { React.Children.toArray( table.map(o=>( <tr> <td>{o.name}</td> <td>{o.city}</td> <td>{o

I am trying to carry a class between li 's with combine each() and eq() methods when buttons clicked. I am using same code with previous and next buttons except i+1 and i-1 but it is returning me different problems. Here is jsFiddle to examine. html: <span class="prev">prev</span> <ul> <li>0</li> <li>1</li> <li class="active">2</li> <li>3</li> <li>4</li> </ul> <span class="next">next</span> jQuery: var li = $('li'); $('.prev').click(function() { li.each(function(i) { if ( $(this).hasClass('active') ) { console.log(i); //this returning current true value = 2 li.removeClass('active'); li.eq(i-1)

[原文链接： http://engineering.richrelevance.com/bandits-recommendation-systems/ 。] [本文链接： http://www.cnblogs.com/breezedeus/p/3775316.html ，转载请注明出处] Bandits for Recommendation Systems 06/02/2014 • Topics: Bayesian , Big data , Data Science by Sergey Feldman This is the first in a series of three blog posts on bandits for recommendation systems. In this blog post, we will discuss the bandit problem and how it relates to online recommender systems. Then, we'll cover some classic algorithms and see how well they do in simulation. A common problem for internet-based companies is: which piece of

Dijkstra算法 1003 Emergency (25 分) As an emergency rescue team leader of a city, you are given a special map of your country. The map shows several scattered cities connected by some roads. Amount of rescue teams in each city and the length of each road between any pair of cities are marked on the map. When there is an emergency call to you from some other city, your job is to lead your men to the place as quickly as possible, and at the mean time, call up as many hands on the way as possible. Input Specification: Each input file contains one test case. For each test case, the first line