YOLOv8 Already? How is it better than v5, try it to see!

YOLO (You Only Look Once) is a popular object detection algorithm used for computer vision applications. The latest version of YOLO, YOLOv8, was released in 2021 and it represents a major upgrade over its predecessor, YOLOv5. In this blog post, we will compare the performance and upgrades of YOLOv8 over YOLOv5.

Performance:
YOLOv8 provides improved performance compared to YOLOv5. This is due to several factors, including the use of a more efficient architecture, the addition of extra convolutional layers, and the use of anchor-based object detection. YOLOv8 also provides faster processing speeds, making it more suitable for real-time object detection applications.

Upgrades:

1. Improved Architecture: YOLOv8 introduces an updated architecture that is more efficient and accurate compared to YOLOv5. This new architecture uses a combination of residual blocks, bottleneck blocks, and inverted residual blocks to improve the accuracy and efficiency of the model.

2. Anchor-based Object Detection: YOLOv8 introduces anchor-based object detection, which provides more accurate and precise object detection compared to YOLOv5. Anchor-based object detection uses anchor boxes to predict the location and size of objects in the image.

3. More Convolutional Layers: YOLOv8 introduces extra convolutional layers, which provide the model with more capacity to learn and improve accuracy.

4. Improved Training: YOLOv8 introduces a new training regime that allows the model to learn more efficiently and achieve higher accuracy. This includes the use of a larger dataset and the use of transfer learning to fine-tune the model.

In short, YOLOv8 represents a major upgrade over YOLOv5 in terms of performance and accuracy. The improved architecture, anchor-based object detection, extra convolutional layers, and improved training regime all contribute to the improved performance of YOLOv8. If you are looking for a powerful and efficient object.