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You may have heard of terms such as deep web, Dark Network (dark web) or hidden network (hidden web), especially in the most recent media. What do they mean? Deep Net is a part of the network, and the shallow network (surface Web) opposition. Shallow web is the Internet search engine can catch that part of the network. According to incomplete statistics, the Internet in fact about 90% of the network is deep net. Because Google can not do such things as form submission, and can not find those wh

Project Introduction Yolo_mark is a test task data set making tool, after the completion of the data format is not VOC or Coco data format, from its name can be seen, it is specifically for the Yolo series of network training to prepare data, Yolo this is very willful, It does not use any of the existing deep learning frameworks to implement his code, but instead writes a pure C lightweight framework-darknet, so its training data is not prepared in t

First spit groove, deep learning development speed is really fast, deep learning framework is gradually iterative, it is really hard for me to engage in deep learning programmer. I began three years ago to learn deep learning, these deep learning frameworks are also a change, from Keras, Theano, Caffe, Darknet, TensorFlow, and finally now to start using Pytorch. I. Variable, derivative Torch.autograd module When the default variable is defined, Requir

This article is reproduced from: http://blog.csdn.net/u011534057/article/details/51244354 Reference Link:http://blog.csdn.NET/tangwei2014 This is after rcnn,fast-rcnn and FASTER-RCNN, RBG (Ross girshick) Great God, another masterpiece, played a very entertaining name: YOLO.Although the current version has some mishap, but the current based on the DL detection of a big pain point, is the speed problem.Its enhanced version of the GPU can run 45fps, simplifying version 155fps. Paper Download: htt

The deep Web is part of the network, and is opposed to a shallow network (surface Web). Shallow web is the Internet search engine can catchTo that part of the network. Incomplete statistics, the Internet in fact about 90% of the network is a deep network. Because Google does notYou can do things like form submissions, and you can't find those pages that aren't directly linked to the top-level domain, or becauseIn order to have robots.txt forbidden to view the website, so the number of shallow ne

refers to malicious behaviors observed outside the organization. Here we only focus on three types of malicious behaviors:Spam, Phishing, and ScanAction. This part of data mainly comes from the following databases: > SPAM: CBL, SBL, SpamCop, WPBL, and UCEPROTECT;> Phishing: SURBL, PhishTank, and hpHosts;> Scanning: Darknet scanners list, Dshield, and OpenBL;2. Security Event Data Security Event data mainly comes from three open network security datab

/object-detection-image-classification-yolo.html) We explain object detection, how Yolo algorithm can help with image classification, and introduce the Open Source neural network framework Darknet. There are a few different algorithms for object detection and they can be split into two groups: Algorithms Based on classification-they work in two stages. in the first step, we're selecting from the image interesting regions. then we're classif

target detection is regression, so a CNN that implements regression does not need a complex design process. Yolo does not choose sliding window or extracting proposal way to train the network, but directly selects the whole graph training model. The advantage of this is that you can better distinguish between the target and the background area, in contrast, the FAST-R-CNN with proposal training methods often mistakenly detect the background area as a specific target. Of course, YOLO has sacrifi

features Multi-scale Training: During training, the model input size is changed every few rounds to make the model robust for different size images. Every 10batches, the model randomly selects a new input image size (320,352,... Multiples of 608,32 because the sampling factor under the model is 32). This training rule forces the model to adapt to different input resolutions. The model is faster for small-size input processing, so the YOLOv2 can adjust speed and accuracy as required. At low reso

65447947?utm_source=itdadaoutm_medium=referral[Net]batch=64The parameters are updated once per batch of samples. Subdivisions=8 If the memory is not large enough, the batch is split into subdivisions sub-batch, and the size of each child batch is batch/Subdivisions In the Darknet code, the batch/Subdivisions is named batch. height=416High width of the input image=416width channels of the input image=3number of channels for the input image momentum=0.9

detected at different resolutions. When the input picture size is relatively small, run faster, the input picture size is larger when the precision is high, so you can YOLO v2 speed and accuracy of the tradeoff.    Experimental Results       Network Improvement faster-draknet19 YOLO v2 is based on a new classification model, somewhat similar to Vgg. YOLO V2 uses 3*3 filter, which increases the number of channels after each pooling. YOLO V2 uses the global average pooling, uses the Batch normil

convolutional neural network on a bongard image, it will first learn the corresponding characteristics of different geometries, each of which can be considered a filter. If there is a corresponding feature, the corresponding classifier is activated. In order to train a feature extraction neural network (NN), I had to create a new dataset and not use the BP image because they were too few and too similar. Synthesizing Datasets To train the feature extraction network, I generated a set of rand

(1) speed/accuracy trade-offs for modern convolutional object detectors Its main consideration is three kinds of detectors (Faster RCNN,R-FCN,SSD) as the meta structure, three kinds of CNN Network (vgg,inception,resnet) as feature extractor, change other parameters such as image resolution, proposals quantity, etc. The tradeoff between accuracy rate and speed of target detection system is studied. (2) Yolo9000:better, Faster, stronger It is an upgraded version of YOLO, which has made some improv

suppression optimization test results. Interested children shoes can be installed according to http://pjreddie.com/darknet/install/instructions to test the YOLO scoring process, very easy to get started. Next, we will focus on the principle of YOLO. 5.1 Integrated Inspection Solutions Yolo's design philosophy follows end-to-end training and real-time detection. Yolo divides the input image into s*s meshes, and if the center of an object falls within

the virtual machine on their own computer installed a Ubuntu14.04, and then install OPENCV, follow the "Ubuntu14.04 installation OpenCV3.0" This tutorial, until make, the whole process did not have any problems, samples inside the program also tried, is functioning properly. Then installed the darknet, and then the OpenCV compiled, in the run YOLO test, the picture is normal bounce out, but there is a problem, is a hint below: Failed to load OpenCL r

changed the filters value to 30. Data/gesture.namesOnly one line, hand 3. Training ./darknet Detector Train Cfg/gesture.data cfg/yolo-gesture.2.0.cfg darknet19_448.conv.23 | Tee Gesture.txt You can also evaluate the results ~ Refer to my previous article: http://blog.csdn.net/shangpapa3/article/details/76687191 4. My question My dataset is in this format, the box is a rotated rectangle, and the build tag program is modified as follows to turn it i

is darknet, change is the network structure of the configuration file, first we look at what it really is: From the above figure we can see that YOLO 2 has 32 layers. Structure is still relatively conventional, mainly in the use of 3*3 convolution, 2*2 of the pooling and 1*1 convolution. In addition to the above three general operations, there are also reorg and route, where route is on the 25 and 28 levels, reorg on the 27 floor. Route: Route layer

This article is reproduced from: http://blog.csdn.net/ch_liu23/article/details/53558549 Recently in the use of YOLO to do the video of the personnel detection, choose YOLO is from the speed of consideration, of course, can also use SSD. Yolo related to see the home page darknet, there are related code and use methods. Because before doing their own data training process appeared various problems, referring to a variety of blogs to run through, now r