yolo caffe

.YOLO implementation method divides an image into an SxS grid (grid cell), which is responsible for predicting the object if the center of an object falls in the grid. Each grid predicts B bounding box, and each bounding box has a confidence value to predict, in addition to its position.This confidence represents the confidence of the object contained in the predicted box and the quasi-double information of the box prediction, the value of which is ca

Summary In the process of training YOLO V2, the system will show some values of evaluation training effect, such as Recall,iou and so on. In order to be afraid of forgetting later, now put their own understanding of these several measures of the way to record.This article first assumes a test set, and then around the test set to introduce the methods of calculation of these measures. the wild Goose and the plane Assuming there is a test set, the imag

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-darkn

Other articles Http://blog.csdn.net/baolinq The last time I wrote an article about using YOLO to train an VOC dataset, the Portal (http://blog.csdn.net/baolinq/article/details/78724314). But you can't always use just one dataset and use a few datasets to see the results. Because I am mainly in the vehicle and pedestrian detection. Just Kitti data set is a public authoritative data set for unmanned driving, including a large number of roads,

Due to the introduction of a previous article on the implementation of their own network layer, but the article difficult, this time I have the simplest image scaling layer for example to implement. Before you explain, there are a few prerequisites you need to master, and that is that you already know how to install Caffe and the directories inside Caffe. First of all, we design the parameters of our layer

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

1, YOLO Training, the main function of loading image data is: Load_data_region (), which contains a lot of image preprocessing, such as a variety of exposure adjustment, image crop and other operations. The specific implementation is as follows: DATA.C data load_data_detection (int n, char **paths, int m, int w, int h, int boxes, int classes, float jitter, float hu E, float saturation, float exposure) {char **random_paths = get_random_paths (path

Recently began to learn YOLO, blog form to record their own learning distance, about installation, background, etc. are not introduced, directly start reading source code: 1. First find the main function in the darknet.c file, see the explanation of the parameters, and if it is YOLO, perform the Run_yolo function: int main (int argc, char **argv) {//test_resize ("data/bad.jpg"); Test_box (); Test_

This article mainly introduces: After some trouble, in the Windows7 64-bit system successfully configured Caffe, the following summarizes the basic configuration process, as well as the configuration process encountered problems.Configuration environment: Windows7 X64 + CUDA7.0 + VS2013 + matlab2014a1. Installing Cuda1.1. Version SelectionAs for the version selection, install the 7.5, 7.0 and 6.5 versions are available, the installation package is vis

Caffe is a very clear and efficient deep learning framework, now has a lot of users, but also gradually formed their own community, the community can discuss related issues. I began to look at the relevant content of deep learning to be able to use Caffe training to test their own data, see a lot of sites, tutorials and blogs, also took a lot of detours, the whole process to comb and summarize, in order to

This tutorial as far as possible, most of the steps have a diagram, if you run an error, please check your own files and the same as on the map, including punctuation ah, space Ah, slash, anti-slash ah, such as small details.This routine is performed under the Win10 64-bit Caffe-master vs2013, and the GPU version has been configured and, if CPU, changed in Cifar10_quick_solver.prototxt# Solver MODE:CPU or GPUSolver_mode:cpuAbstract:

Introduction and use of Caffe-tensorflow conversion Caffe-tensorflow can convert Caffe network definition file and pre-training parameters into TensorFlow form, including TensorFlow network structure source code and NPY format weight file.Download the source code from GitHub and enter the source directory to run convert.py.Its invocation format is Python convert

Objective:Recently in learning deep learning, the first thing to solve is the open source framework of the environment installed. has been studying Google's TensorFlow open source framework, the recent experiment needs to compare with others ' algorithms, the download of other people's code is caffe, so want to build a good caffe environment to run other people's code. There's been a lot of detours in the m

This article describes in detail how to use Caffe to run their own image data for classification.1 first need to install the process see http://www.cnblogs.com/love6tao/p/5706830.html at the same time, according to the above tutorial, generated Caffe.exe2 Build your own data set. Divided into train and Val two datasets, this experiment for 2 classification tasks, one is to include the image of the car, one is not included in the car image, where train

When running the two examples of Caffe, our data comes from the Internet, which is a binary file that is downloaded directly.But most of us use raw picture data (such as. jpg. PNG, etc.), and then we'll look at how to convert the original image data into data that Caffe can run.1. Prepare picture data Caffe after the installation is complete, there will be four.

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

For us to use YOLO for other applications in study, the ultimate goal is definitely not to look at the predictions BoundingBox OK.Specifically for me, we need to get boundingbox and then find the local information of the target. Here's a brief description of how to do it.1. Modify the source codeIn the source code image.c find the Draw_detections () function, print out the position of the box, as follows:2. Record terminal output to text fileThe comma

successful, please refer to the other tutorials to compile samples for testing.Finally, configure the environment variables, we put directly in the system configuration file profile, first open the profile file# sudo vi/etc/profileAdd two lines of code at the end, and if you don't edit with VI, please BaiduExport path=/usr/local/cuda-7.5/bin: $PATHexport ld_library_path=/usr/local/cuda-7.5/lib64: $LD _library_ PATHSave exit until Cuda 7.5 is installed.4, installation CaffeDownload

Installation environment: Ubuntu 16.04lts 64-bit, gcc5.4 gpu1080ti,cuda8.0,cudnn5.1.101. Installing dependent librariessudo apt-get install Libprotobuf-dev libleveldb-dev libsnappy-dev libopencv-dev libhdf5-serial-dev Libboost-all-dev Protobuf-compilersudo apt-get Install Libatlas-base-dev libgflags-dev libgoogle-glog-dev Liblmdb-dev2. Installing CaffeTerminal Input Command:git clone git://github.com/bvlc/caffe.gitThen find the Caffe folder, open and

Caffe is a concise and efficient deep learning framework, the specific introduction can be seen here, Caffe environment configuration process can refer to here, I built the environment when the collection of a lot of information, here to organize a bit, introduce caffe in the environment without cuda how to configure.1. Installing Build-essentialsinstall some bas