You can not select more than 25 topics
Topics must start with a letter or number, can include dashes ('-') and can be up to 35 characters long.
51 lines
2.5 KiB
51 lines
2.5 KiB
--- |
|
comments: true |
|
description: Learn how Ultralytics YOLOv8 AI framework supports detection, segmentation, classification, and pose/keypoint estimation tasks. |
|
keywords: YOLOv8, computer vision, detection, segmentation, classification, pose, keypoint detection, image segmentation, medical imaging |
|
--- |
|
|
|
# Ultralytics YOLOv8 Tasks |
|
|
|
YOLOv8 is an AI framework that supports multiple computer vision **tasks**. The framework can be used to |
|
perform [detection](detect.md), [segmentation](segment.md), [classification](classify.md), |
|
and [pose](pose.md) estimation. Each of these tasks has a different objective and use case. |
|
|
|
<br> |
|
<img width="1024" src="https://raw.githubusercontent.com/ultralytics/assets/main/im/banner-tasks.png"> |
|
|
|
## [Detection](detect.md) |
|
|
|
Detection is the primary task supported by YOLOv8. It involves detecting objects in an image or video frame and drawing |
|
bounding boxes around them. The detected objects are classified into different categories based on their features. |
|
YOLOv8 can detect multiple objects in a single image or video frame with high accuracy and speed. |
|
|
|
[Detection Examples](detect.md){ .md-button .md-button--primary} |
|
|
|
## [Segmentation](segment.md) |
|
|
|
Segmentation is a task that involves segmenting an image into different regions based on the content of the image. Each |
|
region is assigned a label based on its content. This task is useful in applications such as image segmentation and |
|
medical imaging. YOLOv8 uses a variant of the U-Net architecture to perform segmentation. |
|
|
|
[Segmentation Examples](segment.md){ .md-button .md-button--primary} |
|
|
|
## [Classification](classify.md) |
|
|
|
Classification is a task that involves classifying an image into different categories. YOLOv8 can be used to classify |
|
images based on their content. It uses a variant of the EfficientNet architecture to perform classification. |
|
|
|
[Classification Examples](classify.md){ .md-button .md-button--primary} |
|
|
|
## [Pose](pose.md) |
|
|
|
Pose/keypoint detection is a task that involves detecting specific points in an image or video frame. These points are |
|
referred to as keypoints and are used to track movement or pose estimation. YOLOv8 can detect keypoints in an image or |
|
video frame with high accuracy and speed. |
|
|
|
[Pose Examples](pose.md){ .md-button .md-button--primary} |
|
|
|
## Conclusion |
|
|
|
YOLOv8 supports multiple tasks, including detection, segmentation, classification, and keypoints detection. Each of |
|
these tasks has different objectives and use cases. By understanding the differences between these tasks, you can choose |
|
the appropriate task for your computer vision application. |