Introduction to YOLOv8 Practical Chinese Traffic Sign Recognition Course

YOLOv8 Instance Split Practice: Course Introduction

This course is dedicated to guiding students to use the U-Net V8 framework to split instances of custom data sets, with special attention to the driving scenarios. U-Net V8 uses improved backbone, neck and decoupling head to improve gradient flow, and combines new training strategies such as task alignment and distributed focus loss function to enhance detection accuracy. It will also introduce the basic knowledge of case segmentation, performance indicators and the history of YOLO series. The course covers software environment construction, data annotation, format conversion, training optimization and practical application, and finally realizes real-time high-precision target segmentation. It is suitable for computer vision beginners, image processing engineers, deep learning researchers, automatic driving technology developers and visual algorithm designers.

Why use the server to train yolo dataset? I understand the speed!

This section shows the efficient performance of the server when processing large-scale datasets, highlighting its practicability in the fast training process. The processing speed of 4893 data sets is mentioned in the article. The average training time per round is more than 30 seconds, which shows the advantages of the server in the field of machine learning. In addition, the data validation process is also introduced to confirm the quantity matching between the training set and the validation set. For developers and machine learning engineers who pursue training efficiency and accuracy, server training provides a compelling solution.

YOLOV5 environment construction

The course aims to teach zero level students the whole process of using YOLOv5 for custom object detection, including annotation and format conversion of data sets, and explain how to configure YOLOv5 data and model files to achieve migration learning. The method of deploying the model to the edge and end side is explained, and the CPU optimization is realized with OpenMMLab. In addition, the historical background of YOLO series models and the introduction of YOLOv5 model architecture are provided. The course covers the whole contents from environment building, software installation, to model training and deployment, and is suitable for students with basic program development and preliminary understanding of in-depth learning to carry out practical operation learning.

[Three AI practices] YOLO v3 industrial defect detection based on Python

This sharing focuses on the practice of industrial defect detection using UL V3 model in deep learning. UL V3 is an object detection model based on the early Yellow2153 model and improved by integrating watermelon network architecture and jump connection technology. It uses three different size feature maps to capture multi-scale information and optimize detection performance. In addition, the model introduces a residual network structure to improve learning ability, and uses logits instead of softmax in classification. The discussion also covers the Bounding Box regression technique and the loss function composition of the model. Professionals who are interested in or engaged in computer vision, especially in object detection and image processing, will benefit from this presentation.

How does driverless driving appear?

The video mainly focuses on the depth estimation technology, and discusses the technical application and importance of judging the distance of objects through the video analysis of the vehicle camera. The key of depth estimation is to recognize the distance information of each pixel in the image, generate a structure similar to the heat map, and distinguish the color depth to show the object distance. This technology is very important in scenes such as assisted driving and 3D reconstruction, and shows the use of monocular cameras in conjunction with neural network models as a cost-effective alternative to depth estimation. This content has practical guiding significance for the application scenarios that pursue technology and cost balance and for researchers or developers interested in depth perception algorithms.

Overview of the overall framework of neural networks

This lesson focuses on building a neural network algorithm from scratch and taking handwritten font recognition as an example for practice. The process involves input feature processing, hidden layer mapping, weight parameter matrix construction and initialization, and introduces the basic concepts of forward propagation and backward propagation. As a key step in neural network training, back-propagation is difficult and is the core of weight parameter update. Through explanation and code practice, the course gradually demonstrates how to solve multi classification problems, and provides guidance on mathematical formulas and calculation processes. The content is suitable for programmers and learners who want to deeply understand and practice in the field of neural networks.

Why are there so many AI projects failing at present?

When running open source projects and tools, remember to look at this module first!