yolo machine learning

that the accuracy rate of YOLO in detecting small targets is about 8~10% than R-CNN, and the accuracy rate is higher than r-cnn in the detection of large targets. The accuracy of Fast-r-cnn+yolo is the highest, and the accuracy rate is 2.3% higher than that of FAST-R-CNN.5.4 SummaryYolo is a convolutional neural network that supports end-to-end training and testing, and can detect and recognize multiple ta

processing of the size object.Training Details: https://zhuanlan.zhihu.com/p/25045711 The author has trained and tested the Pascal VOC2007 and Pascal VOC2012 datasets. Training 135 rounds, batch size is 64, momentum is 0.9, learning rate delay is 0.0005. Learning schedule for: first round, The learning rate increased slowly from 0.001 to 0.01 (because the model

and the intersection of the Ground Truth, which is the accuracy rate of the detection:Iou=detectionresult⋂groundtruthdetectionresult⋃groundtruth IoU = \frac{detectionresult⋂groundtruth} { Detectionresult⋃groundtruth} As shown in the following illustration:The blue box is: GroundtruthThe Yellow box is: DetectionresultThe green box is: Detectionresult⋂groundtruthThe red box is: Detectionresult⋃groundtruth to say. 1, this article refers to the following bloghttps://sanchom.wordpress.com/tag/avera

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

[Introduction to machine learning] Li Hongyi Machine Learning notes-9 ("Hello World" of deep learning; exploring deep learning) PDF Video Keras Example application-handwriting Digit recognition Step 1

IntroductionThe systematic learning machine learning course has benefited me a lot, and I think it is necessary to understand some basic problems, such as the category of machine learning algorithms.Why do you say that? I admit that, as a beginner, may not be in the early st

This column (Machine learning) includes single parameter linear regression, multiple parameter linear regression, Octave Tutorial, Logistic regression, regularization, neural network, machine learning system design, SVM (Support vector machines Support vector machine), clust

Objective:When looking for a job (IT industry), in addition to the common software development, machine learning positions can also be regarded as a choice, many computer graduate students will contact this, if your research direction is machine learning/data mining and so on, and it is very interested in, you can cons

This column (Machine learning) includes single parameter linear regression, multiple parameter linear regression, Octave Tutorial, Logistic regression, regularization, neural network, machine learning system design, SVM (Support vector machines Support vector machine), clust

Preface: "The foundation determines the height, not the height of the foundation!" The book mainly from the coding program, data structure, mathematical theory, data processing and visualization of several aspects of the theory of machine learning, and then extended to the probability theory, numerical analysis, matrix analysis and other knowledge to guide us into the world of

Objective Machine learning is divided into: supervised learning, unsupervised learning, semi-supervised learning (can also be used Hinton said reinforcement learning) and so on. Here, the main understanding of supervision and unsu

What is machine learning?"Machine learning" is one of the core research fields of artificial intelligence, its initial research motive is to let the computer system have human learning ability to realize artificial intelligence.In fact, since "experience" is mainly in the fo

sixth week. Design of learning curve and machine learning system Learning Curve and machine learning System Design Key Words Learning curve, deviation variance diagnosis method, error a

This column (Machine learning) includes single parameter linear regression, multiple parameter linear regression, Octave Tutorial, Logistic regression, regularization, neural network, machine learning system design, SVM (Support vector machines Support vector machine), clust

In machine learning-Hangyuan Li-The Perceptual Machine for learning notes (1) We already know the modeling of perceptron and its geometrical meaning. The relevant derivation is also explicitly deduced. Have a mathematical model. We are going to calculate the model.The purpose of perceptual

For the performance of four different algorithms in different size data, it can be seen that with the increase of data volume, the performance of the algorithm tends to be close. That is, no matter how bad the algorithm, the amount of data is very large, the algorithm can perform well.When the amount of data is large, the learning algorithm behaves better:Using a larger set of training (which means that it is impossible to fit), the variance will be l

Today I saw in this article how to choose the model, feel very good, write here alone.More machine learning combat can read this article: http://www.cnblogs.com/charlesblc/p/6159187.htmlIn addition to the difference between machine learning and data mining,Refer to this article: https://www.zhihu.com/question/30557267D

Learning notes for "Machine Learning Practice": Implementation of k-Nearest Neighbor algorithms, and "Machine Learning Practice" k- The main learning and research tasks of the last semester were pattern recognition, signal theor

Machine learning and its application 2013 content introduction BooksComputer BooksMachine learning is a very important area of research in computer science and artificial intelligence. In recent years, machine learning has not only been a great skill in many fields of comput

Original: Image classification in 5 Methodshttps://medium.com/towards-data-science/image-classification-in-5-methods-83742aeb3645 Image classification, as the name suggests, is an input image, output to the image content classification of the problem. It is the core of computer vision, which is widely used in practice. The traditional method of image classification is feature description and detection, such traditional methods may be effective for some simple image classification, but the tradit