YOLOV8:
YOLO stands for You Only Look Once, a widely accepted model for object recognition and image segmentation, was developed by Joseph Redmon and Ali Farhadi of the University of Washington. Since its launch in 2015, Yolo has received high praise for its exceptional speed and accuracy.
Ultralytics YOLOv8 represents a breakthrough in object detection technology compared to earlier versions. Firstly, unlike previous yolo variants that have some weaknesses, YOLOv8 is an improved version of former developments to boost the performance and flexibility. YOLOv8 has its strategy to produce a harmony between speed, accuracy and utility that would make it crucial in functions like object recognition and tracking, instance segmentation, image classification and pose estimation.
Yolov8 has made improvements from previous version let take a look on the working of yolov8:
First step is to Install Ultralytics with pip command the start predicting new images and videos with yolov8. It can also used to train model on custom dataset. It can explore tasks like segmentation, classification, pose estimation and tracking.
Comparison of yolov8 from its variants:
Variants of yolov8 are:
Yolov8n:
YOLOv8n offers a harmonious blend of speed and accuracy (mAPval: 37.3), making it ideal for real-time applications. With its balanced performance, it ensures a reasonable level of accuracy without sacrificing processing speed (80.4 ms on A100 TensorRT).
Yolov8s:
YOLOv8s achieves enhanced accuracy (mAPval: 44.9) while balancing processing speed (128.4 ms on A100 TensorRT). It presents a favorable trade-off between speed and precision, making it suitable for accuracy-focused applications without compromising on real-time performance.
YOLOv8m:
YOLOv8m achieves enhanced accuracy (mAPval: 50.2) enhancing its versatility across applications. However, this comes at the expense of increased computational complexity (234.7 ms on A100 TensorRT).
YOLOv8l:
YOLOv8l delivers high accuracy (mAPval: 52.9), ideal for demanding, precision-centric tasks. However, it exhibits longer processing times (375.2 ms on A100 TensorRT) due to its emphasis on accuracy. The trade-off lies in achieving heightened precision at the cost of increased processing duration.
YOLOv8x:
YOLOv8x attains the highest accuracy (mAPval: 53.9) across all models, critical for accuracy-focused tasks. However, this peak of accuracy necessitates a notable increase in computational complexity (479.1 ms on A100 TensorRT). It excels in accuracy-critical applications where enough computational resources are available.
Each variant of Yolov8 has its own significance in terms of balanced speed and accuracy, availability of hardware resources, increased accuracy with complexity. Selecting the most suitable variant is crucial, for that ensure the specific requirements of your project to choose the desired variant of yolov8.
I use the YoloV8n for my own custom dataset for three classes:
This is the confusion metrics of the model:
