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I admit, now I don't know what I'm doing. Yesterday, in the Titan bookstore, I saw a book. At first glance, I was fascinated by its cover. It is a kind of pure, can not be pure, quiet can not be quiet, soft can not be soft, warm can not be warm, yellow. It was the first time I was fascinated by such a color. I gently put the book in her palm, Lolita, this is her pure name. Also pure, there is the bottle of flowers intoxicated in yellow. Slowly, I tou
Keras Installation:It is best to build in the Anaconda virtual environment:Conda create-n Environment Name python=3.6Enter the environment:Source Activate Environment nameInstall Keras:Pip Install KerasPip Install TheanoPip Install tensorflow-gpu==1.2.0If you use Theano as backend, you need to Conda install PYGPU to support parallel and gou operations. If Modulenotfounderror:no module named ' Mkl ' appearsTo demote the MKL in the current environment to 2017:Conda Uninstall MKLConda Install mkl
Darknet is a lightweight, fully C-and Cuda-based, open-source deep learning framework with the main features of easy installation, no dependencies (OpenCV can be used), very good portability, and support for CPU and GPU two computing methods.1. Test source code (generalization process)(1) Test imageA (forecast): load_network (NETWORK.C)---> network_predict (NETWORK.C)---> forward_network (NETWORK.C)---> Forward_ Yolo_layer (YOLO_LAYER.C)----> calc_net
breakthrough, that is, after the frame, he will pop up a dialog box, is this OK or cancel box.It is chosen as a breakthrough, because 1, the following list of data is read from the TXT file back to show, according to this line, you can find out how to get the data you want to display to the dialog box; 2 He is the picture on the frame after the Automatic popup dialog box, which helps you to understand how he gets the mouse click Interactive, Gets how the dialog box opens after the click Interac
When the darknet is integrated into the project, there are some problems, the following record the workaround:Integration steps:First, when the YOLO is compiled, you need to open three switches:#define GPU#define CUDNN#define OPENCVPut the compiled libdarknet.so and darknet.h into the corresponding project folder respectively;Add the corresponding Lib path and include path in CMakeLists.txt;Add the appropriate CPP and HPP as well as the main function
cd /usr/lib/x86_64-linux-gnusudo ln -s ~/anaconda/lib/libpng16.so.16 libpng16.so.16sudo ldconfig# # # Can't succeed #export LD_LIBRARY_PATH=~/anaconda2/lib:$LD_LIBRARY_PATHThis shall be added in~/.bashrcor~/.bash_profile# # #成功Reference: https://stackoverflow.com/questions/32405035/caffe-installation-opencv-libpng16-so-16-linkage-issues/32514285#32514285Compiler darknet appears libpng16.so.16: "[email protected]_1.2.9 ' undefined reference
Today's Internet is plagued by various attacks, which are usually targeted at users or network facilities. A popular method to detect attacks and infected hosts is to detect unused network addresses. Since many network threats are spread randomly,
cottage version, but it can also be used.Cracking command Decompilation command: Step 1./dex2jar. sh classes. dex Step 2 Use jd-gui to open the jar package and view the source code Note: The classes. dex in the first step is from the original apk. Extract the original apk and copy the classes. dex file. Command for unpacking, packaging, and signing: Step 1./apktool d-f com.qxshikong.mm.lolita.apk com. lolita Step 2 modify smali Step 3 package the apk
First, the preface This article mainly uses the YOLO V2 to train own license plate picture data, and can frame the license plate area which exists in the test picture, also is the license plate detection. This article refers to Bowen http://m.blog.csdn.net/qq_34484472/article/details/73135354 and http://blog.csdn.net/zhuiqiuk/article/details/72722227. Ii. Preparatory work First you need to download the properly configured darknet, use the./
1. Download the YOLOv3 of the official website and open the terminal input: Git clone https://github.com/pjreddie/darknetAfter the download is complete, enter: CD darknet, and then enter: Make,When make is finished, download the pre-trained weights file by typing: wget https://pjreddie.com/media/files/yolov3.weights in the terminal, then you can run the detector and enter it in the terminal:. Darknet Detect
First, YOLO installation YOLO Official Website: https://pjreddie.com/darknet/yolo/ 1. Installing Darknet git clone https://github.com/pjreddie/darknet cd darknet make 2. Download the weight file pre-trained weight wget https://pjreddie.com/media/files/yolo.weights 3. Testing ./d
enough to see. Here the address is posted first:https://arxiv.org/abs/1612.082422,yolo Installation1, install the Reserve library1) Install the Git toolYum Install git2) Install BUNZIP2Yum Install-y bzip23) Installing GCCYum Install "gcc-c++.x86_64"2. Download the installation package1) Download and compile the installation packagegit clone https://github.com/pjreddie/darknetcd darknet make2) Download pre-trained hyper-parameters, also weightswget ht
It's nice to have hundreds of people who have successfully used YOLO to process their datasets through my tutorials. Recently, the CNN model has been used to do image two classification, but suffers from poor results, so the image classification problem as a target recognition problem. Do target recognition selected YOLO (you just look once), a recently introduced method, the outstanding advantage is speed. Looking up the internet about the YOLO of training their own data sets of methods, most
in the code, the code in the VOC Training dataset path to its own training data set path 5. Convert 20 categories to 1 categories 1) cfg/voc.data file: Classes changed to 1. Names=data/svt.names. Svt.names This file exists in the Darknet directory in the Data folder, create a svt.names, plus the content, of course, name and path can be defined by themselves. The number of rows in this file is the same as the number of classes, and each row is the na
Vocdevkit2007_train.txt voc_label.py2007_val.txt voctest_06-nov-. Tar2012_train.txt voctrainval_06-nov-. Tar2012_val.txt voctrainval_11-may-. TarWe can combine the files we really want to train into one:cat 2007_train.txt 2007_val.txt 2012_*.txt > Train.txt3) Modify the data that the configuration points to (Pascal data)In the Cfg/voc.data, point to the configuration data:1 - 2 Train = train.txt 3 valid = 2007_test.txt 4 names = data/voc.names 5 backup = Backup4) Download w
Requirements have been installed Cuda 8.0 and OpenCV3.1.0YOLO Official websiteConfigure Darknetgit clone https://github.com/pjreddie/darknet cd darknet makeIf there is no error input./darknet Get output./darknet Description Darknet Configuration SucceededOpen the Makefil
Analysis ProcessFirst, we will analyze the training commands of YOLO (the source code of YOLO is written in C ++ ):./Darknet detector train CFG/VOC. Data CFG/yolo-voc.cfg darknet19_448.conv.23Here we can see that the parameter argv [] in the main function of Yolo corresponds to argv [0]-> Darknet argv [1]-> detector argv [2]-> train ..... (For the rest, we can see from here that the main function of Yolo mu
Author: Mu LingDate: November 2016.Blog connection: http://blog.csdn.net/u014540717 In the previous article Training VOC datasets with the YOLOV2 model, we tried to train the VOC dataset with YOLOv2, but I wanted to train my own data set, so YOLOv2 how to do fine-tuning. Let's do it one step at a- 1 preparing data 1.1 building hierarchies First create a folder under the Darknet/data folder fddb2016, the file hierarchy is as follows --fddb2016 --an
). We add a custom convolution layer (blue) after the network and use convolutional cores (green) to perform predictions.Perform a single prediction of categories and locations at the same timeHowever, the convolution layer reduces the spatial dimension and resolution. So the above model can only detect larger targets . to solve this problem, we perform independent target detection from multiple feature maps. The multi-scale feature map is used to detect independently. Use multi-scale feature ma
. Multi-scale Training allows YOLO v2 to make a balanced selection of speed and accuracy, 228x228 input YOLO can reach 90fps, can be applied to smaller GPUs (e.g. we do embedded) for real-time monitoring and high frame rate video detection. The subscript lists the accuracy and speed comparison of the YOLO V2, which shows the speed advantage of the YOLO. 2. Faster YOLO v2 not only to pursue the accuracy rate, more importantly, speed, robot control, autonomous driving technology, it must rely on
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