YOLO v4 or YOLO v5 or PP-YOLO?

Object detection is a computer vision task that involves predicting the presence of one or more objects, along with their classes and bounding boxes. YOLO (You Only Look Once) is a state of art Object Detector which can perform object detection in real-time with a good accuracy.

The first three YOLO versions have been released in 2016, 2017 and 2018 respectively. However, in 2020, within only a few months of period, three major versions of YOLO have been released named YOLO v4, YOLO v5 and PP-YOLO. The release of YOLO v5 has even made a controversy among the people in machine learning community.

Additionally, this has caused a dilemma in the minds of people who are going to start their machine learning projects. In this article, we will discuss the reason for these many new YOLO releases, while emphasizing their originality, authorship, performance and the major improvements, helping people to choose the most appropriate version for their project.

How YOLO evolved

YOLO has been first introduced in 2016 and it was a milestone in object detection research due to its capability of detecting objects in real-time with a better accuracy.

It was proposed by Joseph Redmon, a graduate from the University of Washington. The paper describing YOLO won the the OpenCV People’s Choice Award at the Conference on Computer Vision and Pattern Recognition (CVPR) in 2016.

#machine-learning #yolo #yolov5 #yolov4 #object-detection #deep learning

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YOLO v4 or YOLO v5 or PP-YOLO?

Object detection is a computer vision task that involves predicting the presence of one or more objects, along with their classes and bounding boxes. YOLO (You Only Look Once) is a state of art Object Detector which can perform object detection in real-time with a good accuracy.

The first three YOLO versions have been released in 2016, 2017 and 2018 respectively. However, in 2020, within only a few months of period, three major versions of YOLO have been released named YOLO v4, YOLO v5 and PP-YOLO. The release of YOLO v5 has even made a controversy among the people in machine learning community.

Additionally, this has caused a dilemma in the minds of people who are going to start their machine learning projects. In this article, we will discuss the reason for these many new YOLO releases, while emphasizing their originality, authorship, performance and the major improvements, helping people to choose the most appropriate version for their project.

How YOLO evolved

YOLO has been first introduced in 2016 and it was a milestone in object detection research due to its capability of detecting objects in real-time with a better accuracy.

It was proposed by Joseph Redmon, a graduate from the University of Washington. The paper describing YOLO won the the OpenCV People’s Choice Award at the Conference on Computer Vision and Pattern Recognition (CVPR) in 2016.

#machine-learning #yolo #yolov5 #yolov4 #object-detection #deep learning

Yolo v5 Object Detection Tutorial

Object Detection is a task in Artificial Intelligence that focuses on detecting objects in images. Yolo V5 is one of the best available models for Object Detection at the moment. The great thing about this Deep Neural Network is that it is very easy to retrain the network on your own custom dataset.

In this article, I will cover how to train a Yolo V5 Object Detection model. I will focus on how to get started quickly and easily, rather than on tuning the hyper parameters of the model.

How do you train a custom Yolo V5 model?

To train a custom Yolo V5 model, these are the steps to follow:

1. Set up your environment
2. Set up the data and the directories
3. Set up the configuration YAML files
4. Train the model to learn how to detect objects
5. Use your custom Yolo V5 model to detect objects on the test data

#machine-learning #object-detection #artificial-intelligence #data-science #yolo v5

Custom Object Detection with YOLO V5

Create Yolo v5 custom object detection model to recognize road signs into different categories. It is customizable, based on requirements we can customize the yolo model

Custom Object Detection with YOLO V5

Object detection is one of the most common tasks of computer vision. It is the basis of understanding and working with the scene.

From simple applications like identifying objects to complex tasks like self-driving cars all make use of object detection for understanding different scenarios and making decisions based on them. Security cameras and even modern-day smartphones all have these capabilities built-in with them for various tasks.

What is Object Detection

• The task of knowing what’s present in an image is classification.
• The task of knowing where a certain thing is present in an image is localization
• Finally, the task of knowing what is present in the screen and also where it is present is known as object detection. You can call it the combination of both image classification and localization.

#machine-learning #ai #artificial-intelligence #yolo

How to Train YOLO v5 on a Custom Dataset

YOLOv5 is the latest evolution in the YOLO family of object detection models. It’s the first YOLO implementation native to PyTorch (rather than Darknet) and emphasizes ease of use and quickness of training and inference. This YOLOv5 tutorial shows you how to train the model on your own dataset in Python.

GitHub repo (for additional details, e.g. passing a video file or webcam for inference): https://github.com/ultralytics/yolov5

#python #yolo #opencv #machine-learning #programming

Yolo V4 on iPhone 12 Pro

Yolo V4 on iPhone 12 Pro

Check out the course here: https://www.computervision.zone/courses/computer-vision-mobile-apps/

#yolo #data-science