【解释】

tree的两个bounding boxes 都要保留,因为交并比小于0.5;car 0.73保留;pedestrain 0.98保留;motorcycle 0.58保留。一共5个。

【解释】

5个anchor box, 一个anchor box 对应(1+4+20)个标签,所以output volume 是 19*19*5*25

课程四(Convolutional Neural Networks),第三 周(Object detection) —— 1.Practice questions:Detection algorithms的更多相关文章

  1. 课程四(Convolutional Neural Networks),第一周(Foundations of Convolutional Neural Networks) —— 3.Programming assignments:Convolutional Model: application

    Convolutional Neural Networks: Application Welcome to Course 4's second assignment! In this notebook ...

  2. 课程四(Convolutional Neural Networks),第一周(Foundations of Convolutional Neural Networks) —— 2.Programming assignments:Convolutional Model: step by step

    Convolutional Neural Networks: Step by Step Welcome to Course 4's first assignment! In this assignme ...

  3. 课程四(Convolutional Neural Networks),第二 周(Deep convolutional models: case studies) —— 0.Learning Goals

    Learning Goals Understand multiple foundational papers of convolutional neural networks Analyze the ...

  4. 课程四(Convolutional Neural Networks),第二 周(Deep convolutional models: case studies) —— 2.Programming assignments : Keras Tutorial - The Happy House (not graded)

    Keras tutorial - the Happy House Welcome to the first assignment of week 2. In this assignment, you ...

  5. 课程四(Convolutional Neural Networks),第二 周(Deep convolutional models: case studies) ——3.Programming assignments : Residual Networks

    Residual Networks Welcome to the second assignment of this week! You will learn how to build very de ...

  6. 课程四(Convolutional Neural Networks),第一周(Foundations of Convolutional Neural Networks) —— 0.Learning Goals

    Learning Goals Understand the convolution operation Understand the pooling operation Remember the vo ...

  7. 课程四(Convolutional Neural Networks),第二 周(Deep convolutional models: case studies) —— 1.Practice questions

    [解释] 应该是same padding 而不是 valid padding . [解释] 卷积操作用的应该是adding additional layers to the network ,而是应该 ...

  8. 课程四(Convolutional Neural Networks),第一周(Foundations of Convolutional Neural Networks) —— 1.Practice questions:The basics of ConvNets

    [解释] 100*(300*300*3)+ 100=27000100 [解释] (5*5*3+1)*100=7600 [中文翻译] 您有一个输入是 63x63x16, 并 将他与32个滤波器卷积, 每 ...

  9. 课程四(Convolutional Neural Networks),第四 周(Special applications: Face recognition & Neural style transfer) —— 2.Programming assignments:Art generation with Neural Style Transfer

    Deep Learning & Art: Neural Style Transfer Welcome to the second assignment of this week. In thi ...

  10. 课程四(Convolutional Neural Networks),第三 周(Object detection) —— 2.Programming assignments:Car detection with YOLOv2

    Autonomous driving - Car detection Welcome to your week 3 programming assignment. You will learn abo ...

随机推荐

  1. [leetcode]282. Expression Add Operators 表达式添加运算符

    Given a string that contains only digits 0-9 and a target value, return all possibilities to add bin ...

  2. pythone函数基础(10)MD5加密

    导入hashlib模块import hashlibs='yulin123456's.encode()#把数字转换成bytes类型m=hashlib.md5(s.encode())print(m.hex ...

  3. 交叉编译bash

    1 下载bash版本:[version 4.2.53]地址:http://ftp.gnu.org/gnu/bash/ 2 解压将下载的bash压缩包解压,命令: # mkdir /home/carri ...

  4. 关于前端设置cookie

    cookie既可以后端设置也可以在前端设置,例如登陆/注册功能,每次都要向服务器请求用户数据,这种就可以把cookie放到前端储存起来. 当网页要发http请求时,浏览器会先检查是否有相应的cooki ...

  5. Centos Firefox中文乱码

    解决CentOS Firefox 中文乱码问题,执行以下命令 Centos 6 # yum -y groupinstall chinese-support 重启电脑即可. Centos 7 yum - ...

  6. Find Common Characters LT1002

    Given an array A of strings made only from lowercase letters, return a list of all characters that s ...

  7. idea配置servlet记录,tmocat当服务器,学习

    没整理图片,将就看吧, Mac10.11.6 idea2018.1.3 servlet+tmocat9 遇到问题: 端口错误 java.rmi.server.ExportException: Port ...

  8. Java 浮点数相加

    刚刚遇到个需求,需要对金额求和,上线的时候才知道这时个,这个字段是个小数. 随手就改了个Double ,然后,跑下,没啥问题,直接上线了 然后,就fuck 了 加出一大堆的小数,大概是这样的 pack ...

  9. js 时间戳转日期

    timestampToTime(10位时间戳) function timestampToTime(timestamp) { var date = new Date(timestamp * 1000); ...

  10. OpenGL Compute Shader靠谱例子及读取二进制Shader,SPIR-V

    学OpenGL以来一直苦恼没有像DX那样可以读取二进制Shader使用的方法,除去有时不想公开自己写的牛逼Shader的心理(虽然目前还从没写过什么牛逼的Shader), 主要是不用现场编译,加快读取 ...