YOLOV8, Is It Better than Its Predecessor?

Exploring the Improvements and Advancements of YOLOV8 over its Forerunner

Introduction of YOLOV8

YOLOV8, the latest state-of-the-art YOLO (You Only Look Once) model, offers remarkable capabilities for various computer vision tasks such as object detection, image classification, and instance segmentation. Developed by Ultralytics, the same team behind the influential YOLOV5 model, YOLOV8 represents a significant advancement in the YOLO series.

With YOLOV8, Ultralytics has introduced several architectural and developer experience enhancements, building upon the foundation laid by its predecessor. These improvements aim to deliver superior performance and address the evolving needs of the computer vision community.

It’s important to note that YOLOV8 is an actively developed model. As of writing this post, Ultralytics continues to refine and add new features to the model while actively engaging with the community. This commitment to ongoing development ensures that YOLOV8 receives long-term support and benefits from valuable feedback and contributions from users.

Ultralytics has established a collaborative approach, actively working with the community to optimize the model and make it the best it can be. By embracing community input and incorporating advancements in the field of computer vision, YOLOV8 aims to push the boundaries of object detection and related tasks, further cementing its position as a leading solution in the field.

The Evolution of YOLO: From One Shot to Eight

The YOLO (You Only Look Once) series of models has gained immense popularity in the world of computer vision. Its success can be attributed to its impressive accuracy while maintaining a compact model size. What makes YOLO even more appealing is its ability to be trained on a single GPU, making it accessible to a wide range of developers. This means that it can be deployed on edge hardware or in the cloud at a low cost.

Since its inception in 2015 by Joseph Redmond, YOLO has been embraced and nurtured by the computer vision community. In its early versions (1–4), YOLO was implemented in C code using a custom deep learning framework called Darknet, developed by Redmond.

Glenn Jocher, the creator of YOLOV8 at Ultralytics, started by closely studying the YOLOV3 repository in PyTorch, a popular deep-learning framework from Facebook. As the training in the repository improved, Ultralytics eventually released its own model called YOLOV5.

YOLOV5 quickly gained recognition as the leading repository worldwide, thanks to its flexible and user-friendly Python structure. This structure allowed the community to introduce new modeling improvements and quickly share them across repositories that utilized similar PyTorch methods.

In addition to its strong foundational model, the YOLOV5 team has shown a commitment to fostering a healthy software ecosystem around their creation. They actively address issues and continuously enhance the capabilities of the repository based on the feedback and needs of the community.

Over the past two years, several models have branched off from the YOLOV5 PyTorch repository, including Scaled-YOLOV4, YOLOR, and YOLOV7. Furthermore, other models have emerged globally with their own implementations based on PyTorch, such as YOLOX and YOLOV6. Each iteration of the YOLO model brings new state-of-the-art techniques that push the boundaries of accuracy and efficiency.

In the last months, Ultralytics has focused on extensive research to develop the latest state-of-the-art version of YOLO, known as YOLOV8. This version, launched on January 10th, 2023, represents a significant milestone in the YOLO series. It introduces advancements aimed at further enhancing performance and pushing the boundaries of object detection capabilities.

YOLOV8: The Ultimate Solution for Object Detection

YOLOV8 is not just an improvement over previous versions of YOLO, but a complete overhaul that introduces new features, architectures, and datasets. Here are some of the reasons why YOLOV8 is the best choice for your future object detection project.

One of the main reasons why you should use YOLOV8 is its high rate of accuracy measured by two popular benchmarks: COCO and Roboflow 100. COCO is a large-scale dataset that contains over 200,000 images with 80 object categories. It is widely used to evaluate the performance of object detection models on general scenes. Roboflow 100 is a new dataset that consists of 100 subsets of images from different domains, such as medical, aerial, or sports. It is designed to test the robustness and generalization of object detection models on various task-specific scenarios.

YOLOV8 achieves impressive results on both benchmarks. For example, the YOLOV8M model — the medium model — achieves a 50.2% mean average precision (mAP) when measured on COCO. This means that it correctly detects and classifies half of the objects in the images on average. This is comparable to some of the best models in the field, such as EfficientDet or CenterNet. When evaluated against Roboflow 100, YOLOV8 scored substantially better than YOLOV5, its predecessor. It outperformed YOLOV5 on 75 out of 100 subsets, showing that it can handle diverse and challenging domains better. We will provide more details on the performance analysis of YOLOV8 later in the article.

Another reason why you should use YOLOV8 is its developer-convenience features, which make it easy to use and customize. Unlike other models where you have to run multiple Python files to perform different tasks, such as data preparation, training, or inference, YOLOV8 comes with a command-line interface (CLI) that simplifies the workflow. You can train a model on your own data with just one command, or use one of the pre-trained models available for download. You can also adjust various parameters and options with simple flags and arguments. For example, you can change the model size, the number of epochs, the batch size, or the learning rate with a few keystrokes.

In addition to the CLI, YOLOV8 also provides a well-structured Python package that allows you to access and modify the model components more easily. You can import the package in your code and use its functions and classes to create your own custom models or applications. For example, you can use the `Model` class to define your own network architecture, or use the `Dataset` class to load your own data format. You can also use the `Detector` class to run inference on images or videos and get the detection results as Python objects.

The last reason why you should use YOLOV8 is its large and growing community around it. YOLO is one of the most popular and influential object detection algorithms in computer vision. It has been cited over 20,000 times in academic papers and used in many real-world applications. Many computer vision experts know about YOLO and how it works, and there is plenty of guidance online about using YOLO in practice. Although YOLOV8 is new as of writing this piece, there are already many tutorials and guides online that can help you get started with it. You can also find many examples and projects that use YOLOV8 on GitHub or other platforms.

Summary

YOLOV8, the latest state-of-the-art model in the YOLO series, represents a significant advancement over its predecessor. Developed by Ultralytics, YOLOV8 offers remarkable capabilities for computer vision tasks, with architectural and developer experience enhancements. It achieves high accuracy on popular benchmarks such as COCO and Roboflow 100, outperforming YOLOV5 and other models.

The developer-convenience features, including a user-friendly command-line interface (CLI) and a well-structured Python package, make it easy to use and customize. Additionally, YOLOV8 benefits from a large and growing community, with ample online resources and guidance available.

Overall, YOLOV8 emerges as the ultimate solution for object detection, pushing the boundaries of performance and solidifying its position as a leading model in the field of computer vision.