Anyone who has done object detection will be familiar with these three algorithms, or at least have heard of them.What are the characteristics of these three algorithms?Why can't they replace each other?Next, we will analyze it slowly.
Before introducing specific algorithms, let's look at commonly used data sets. pascal voc contains 20 classesAmong them, voc 07：9,963 picture contains 24,640 marked objectsVoc 12: The test data set is not disclosed.
2016 is over and the development of front-end technology will enter a new stage. What will it bring us in the future?Here, I would like to express my personal opinion. Do not spray it if you don't like it.
As far as the development of the front-end mainstream technology framework is concerned, inside has developed extremely fast in the past few years. While filling in the gaps and deficiencies of the original technology framework, it is also gradually maturing.
--- by Liuhua.chen
The author needs to deal with the problem of comparing similar pictures in the project a few days ago, and successfully completed the function development after searching data on the network and some personal research.We hereby share the following:
First, the mainstream framework OpenCV is adopted, which provides the following methods for comparing pictures.
1, PSNR Peak Signal to Noise Ratio PSNR is the most common and widely used objective evaluation index for images.
Machine Learning (Zhi-Hua Zhou) Reference Answer Chapter 10 Dimension Reduction and Metric Learning Machine Learning (Zhi-Hua Zhou Watermelon Book) Reference Answer List
http://blog.csdn.net/icefire_tyh/article/details/52064910 1. The K-neighbor classifier is programmed and compared with decision tree classification boundary on watermelon dataset 3.0 Alpha. http://blog.csdn.net/icefire_tyh/article/details/52243081 2. make err, err, err, err * respectively represent the expected error rates of the nearest neighbor classifier and Bayesian optimal classifier.
Naive Bayesian Harbin Engineering University -537 algorithm principle: code implementation: First import the library to be used: numpy, re, random import numpy as np import re import random Define a text_parse function and parse the document by word segmentation (dividing the whole document into words) to obtain a list of words longer than 2. def text_parse(big_string): list_of_tokens = re.split(r"\W*", big_string) # W为大写 return [tok.lower() for tok in
Introduction I read an article earlier. A foreigner programmer wrote some amazing Shell scripts, including sending text messages to his wife after work late, automatically flushing Coffee, automatically scanning email from a DBA, etc.So I also want to do some interesting things with what I have learned.My thoughts are as follows: First I write a scrapy script to capture jokes on a website Then write a Shell script to automatically
The sys.argv variable is a list of strings. sys.argv is used to obtain command line arguments.Remember, the name of the script is always the first parameter in the sys.argv list.Python starts counting from 0, not 1.Sys.argv represent that file path of the code itself;For example, if you enter " pythontest.py-help" on the CMD command line, sys.argv means " test.py"
list and string are converted to each other.
Reference Guide for Metric Learning
metric learning refers to Distance Metric Learning, abbreviated as DML, which is widely used in image retrieval and classification, face recognition, human activity recognition and pose estimation in computer vision, text analysis and some other fields such as music analysis, automated project debugging, microarray data analysis, etc. metric learning was proposed by Eric P. Xing, Andrew Y. Ng and others in NIPS 2003.
This is not a new word.
I've read a lot of books and been in actual combat, but I always want to tell the CNN process in common language. Today I have the honor to share it with you.
First of all, Convolutional Neural Network is a neural network with at least one layer. The function of this layer is to calculate the convolution of input F and configurable convolution stone G to generate output.The purpose of convolution is to apply convolution stone to all points of a tensor and generate a new filtered tensor by sliding convolution stone.