The latest addition to the YOLO algorithm series is YOLOv8, a cutting-edge model that continues the legacy of the renowned family of object detection and classification models in the field of Computer Vision (CV). With YOLOv8, users can expect state-of-the-art results for image and video analytics within an easily implementable framework.
Here is an overview of what this article covers:
1. The evolution of the YOLO algorithms
2. Improvements and enhancements in YOLOv8
3. Implementation details and tips
4. Applications
About us: Viso.ai offers the leading end-to-end Computer Vision Platform, Viso Suite, designed to help various organizations embark on their computer vision journey and swiftly implement state-of-the-art models for a range of industrial applications. Request a demo to learn more.
What is YOLO?
The You Only Look Once (YOLO) algorithm, introduced in 2015 by Joseph Redmon, Santosh Divvala, Ross Girshick, and Ali Farhadi, revolutionized real-time object detection by surpassing its predecessor, the Region-based Convolutional Neural Network (R-CNN).
YOLO is a single-shot algorithm that classifies objects in a single pass using a neural network to predict bounding boxes and class probabilities based on a full image input.
The YOLO family of models continues to evolve, with various research teams releasing different versions. YOLOv8 is the latest iteration, building upon the successes of its predecessors. The following section provides a brief overview of the historical versions and their advancements.
A Brief History of YOLO
Before delving into the evolution of YOLO, it’s essential to understand the basics of how a typical object detection algorithm operates.
The diagram below illustrates the fundamental mechanics of an object detection model.
[Image: Object Detection model architecture]
The architecture comprises a backbone, neck, and head. The backbone, a pre-trained Convolutional Neural Network (CNN), extracts low, medium, and high-level feature maps from an input image. The neck merges these feature maps using path aggregation blocks like the Feature Pyramid Network (FPN) before passing them to the head for object classification and bounding box prediction.
The head can consist of one-stage or dense prediction models like YOLO or Single-shot Detector (SSD), or two-stage or sparse prediction algorithms like the R-CNN series.
Table: YOLO Version Summary
[Table showing different versions of YOLO along with release years, authors, tasks, and papers]
YOLOv1: The First Iteration
YOLOv1 marked a significant advancement in object detection by framing the problem as a regression task to predict bounding boxes and class probabilities in a single image pass. By dividing images into grids and calculating confidence scores and bounding boxes for each grid cell, YOLOv1 achieved a mean average precision (mAP) of 63.4 and an inference speed of 45 frames per second on the Pascal Visual Object Classes 2007 dataset.
YOLOv2: Enhanced Detection Capabilities
In 2016, Joseph Redmon and Ali Farhadi introduced YOLOv2, capable of detecting over 9000 object categories. This version introduced anchor boxes, predefined bounding boxes known as priors that assist in pinpointing an object’s ideal position. YOLOv2 achieved a 76.8 mAP at 67 FPS on the VOC 2007 dataset.
YOLOv3: Improved Accuracy
Joseph Redmon and Ali Farhadi’s YOLOv3, published in 2018, boasted higher accuracy than its predecessors, achieving an mAP of 28.2 in just 22 milliseconds. This model utilized Darknet-53 as the backbone with logistic classifiers and Binary Cross-entropy (BCE) loss for class predictions.
YOLOv4: Optimization for Speed and Accuracy
In 2020, Alexey Bochkovskiy and team introduced YOLOv4, which introduced the concepts of Bag of Freebies (BoF) and Bag of Specials (BoS). BoF comprised techniques that enhanced accuracy without increasing inference cost, while BoS methods significantly improved accuracy with a slight rise in computation. YOLOv4 achieved a 43.5 mAP at approximately 65 FPS on the MS COCO dataset.
YOLOv5: PyTorch Implementation
Ultralytics released YOLOv5 in 2020 as a PyTorch implementation, making it easy to train and use. The model utilized a Cross-stage Partial (CSP) Connection block in the backbone for improved gradient flow and opted for YAML files over the CFG file for model configurations.
YOLOv6: Anchor-Free Detection
In 2022, Meituan introduced YOLOv6, targeting industrial applications with enhanced performance. The model featured anchor-free detection and a decoupled head, enabling one head for classification and another for regression. YOLOv6 variants achieved significant mAP scores on the COCO dataset.
YOLOv7: Fast and Accurate Detection
Researchers released YOLOv7 in July 2022, an open-source model that set new benchmarks for object detection speed and accuracy. It utilized the Extended Efficient Layer Aggregation Network (E-ELAN) as its backbone to improve feature learning efficiency and employed compound scaling for concatenation-based models to cater to various inference speeds.
YOLOv8: The Latest Iteration
In January 2023, Ultralytics unveiled YOLOv8, the latest addition to the YOLO series. While lacking an official research paper, YOLOv8 boasted higher accuracy and faster speed. Notably, the YOLOv8(medium) variant achieved a 50.2 mAP score at 1.83 milliseconds on the COCO dataset with A100 TensorRT. YOLOv8 also introduced a Python package and CLI-based implementation, enhancing user-friendliness and development ease.
For in-depth insights into YOLOv8’s capabilities and significant developments, refer to the original article.
[Image: Enterprise computer vision pipeline with Viso Suite]
What is YOLO?
You Only Look Once (YOLO) is an object-detection algorithm that was introduced in 2015. It is considered a revolutionary approach to real-time object detection, surpassing its predecessor, the Region-based Convolutional Neural Network (R-CNN).
YOLO is a single-shot algorithm that directly classifies objects in a single pass by having only one neural network predict bounding boxes and class probabilities using a full image as input.
The YOLO family of models has evolved over time, with various versions released by different research teams. The latest iteration, YOLOv8, continues to push the boundaries of object detection and classification models.
A Brief History of YOLO
Before diving into the evolution of YOLO, it is essential to understand the basics of how a typical object detection algorithm works. The diagram below illustrates the essential components of an object detection model.
[Image: Object Detection model architecture]
The architecture consists of a backbone, neck, and head. The backbone, a pre-trained Convolutional Neural Network (CNN), extracts low, medium, and high-level feature maps from an input image. These feature maps are then processed by the neck, which merges them using path aggregation blocks like the Feature Pyramid Network (FPN). Finally, the processed features are passed to the head for object classification and bounding box prediction.
YOLO has seen several iterations over the years, each introducing new features and improvements. Here is a summary of the different versions of YOLO and their key characteristics:
– YOLOv1 (2015): Introduced the concept of framing object detection as a regression task and achieved significant advancements in real-time object detection.
– YOLOv2 (2016): Enhanced detection capabilities by introducing anchor boxes for more precise object localization.
– YOLOv3 (2018): Improved accuracy with higher mAP scores and faster inference speeds.
– YOLOv4 (2020): Optimized speed and accuracy with the introduction of Bag of Freebies (BoF) and Bag of Specials (BoS).
– YOLOv5 (2020): Implemented as a PyTorch model with improved gradient flow and YAML configuration files.
– YOLOv6 (2022): Introduced anchor-free detection and a decoupled head for better performance.
– YOLOv7 (2022): Set new benchmarks for object detection speed and accuracy.
– YOLOv8 (2023): The latest iteration with higher accuracy and faster speed, featuring a Python package and CLI-based implementation.
For a detailed analysis of YOLOv8’s tasks, key developments, and implementation steps, refer to the original article.
[Image: Smart city scene computer vision deep learning networks]
What is YOLO?
YOLOv8 is the latest model in the YOLO algorithm series, a well-known family of object detection and classification models in the Computer Vision (CV) field. With its state-of-the-art results for image and video analytics, YOLOv8 continues the legacy of providing high-quality performance with an easy-to-implement framework.
In this article, we will explore:
– The evolution of the YOLO algorithms
– Improvements and enhancements in YOLOv8
– Implementation details and tips
– Applications
About us: Viso.ai offers the world’s leading end-to-end Computer Vision Platform Viso Suite, helping organizations implement state-of-the-art models quickly and cost-effectively for various industrial applications. Request a demo to learn more.
[Image: Enterprise computer vision pipeline with Viso Suite]
What is YOLO?
YOLOv8 is the newest model in the YOLO algorithm series – the most well-known family of object detection and classification models in the Computer Vision (CV) field. With the latest version, the YOLO legacy lives on by providing state-of-the-art results for image or video analytics, with an easy-to-implement framework.
In this article, we’ll discuss:
– The evolution of the YOLO algorithms
– Improvements and enhancements in YOLOv8
– Implementation details and tips
– Applications
About us: Viso.ai offers the world’s leading end-to-end Computer Vision Platform Viso Suite. Our solution helps several leading organizations start with computer vision and implement state-of-the-art models quickly and cheaply for various industrial applications. Get a demo.
[Image: Satellite image analysis]
What is YOLO?
YOLOv8 is the newest model in the YOLO algorithm series – the most well-known family of object detection and classification models in the Computer Vision (CV) field. With the latest version, the YOLO legacy lives on by providing state-of-the-art results for image or video analytics, with an easy-to-implement framework.
In this article, we’ll discuss:
– The evolution of the YOLO algorithms
– Improvements and enhancements in YOLOv8
– Implementation details and tips
– Applications
About us: Viso.ai offers the world’s leading end-to-end Computer Vision Platform Viso Suite. Our solution helps several leading organizations start with computer vision and implement state-of-the-art models quickly and cheaply for various industrial applications. Get a demo.
