Ultralytics YOLO11 Tasks

YOLO11 is an AI framework that supports multiple computer vision tasks. The framework can be used to perform detection, segmentation, obb, classification, and pose estimation. Each of these tasks has a different objective and use case.

Watch: Explore Ultralytics YOLO Tasks: 물체 감지, Segmentation, OBB, Tracking, and Pose Estimation.

탐지

Detection is the primary task supported by YOLO11. 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. YOLO11 can detect multiple objects in a single image or video frame with high accuracy and speed.

탐지 예시

세분화

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. YOLO11 uses a variant of the U-Net architecture to perform segmentation.

세분화 예시

분류

Classification is a task that involves classifying an image into different categories. YOLO11 can be used to classify images based on their content. It uses a variant of the EfficientNet architecture to perform classification.

분류 예시

포즈

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. YOLO11 can detect keypoints in an image or video frame with high accuracy and speed.

포즈 예시

OBB

Oriented object detection goes a step further than regular object detection with introducing an extra angle to locate objects more accurate in an image. YOLO11 can detect rotated objects in an image or video frame with high accuracy and speed.

방향 탐지

결론

YOLO11 supports multiple tasks, including detection, segmentation, classification, oriented object detection 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.

자주 묻는 질문

What tasks can Ultralytics YOLO11 perform?

Ultralytics YOLO11 is a versatile AI framework capable of performing various computer vision tasks with high accuracy and speed. These tasks include:

감지: 감지: 이미지 또는 비디오 프레임에서 객체 주위에 경계 상자를 그려 객체를 식별하고 위치를 파악합니다.
세분화: 의료 영상과 같은 애플리케이션에 유용한 콘텐츠에 따라 이미지를 여러 영역으로 세분화하는 기능입니다.
분류: EfficientNet 아키텍처의 변형을 활용하여 콘텐츠에 따라 전체 이미지를 분류합니다.
포즈 추정: 이미지 또는 비디오 프레임에서 특정 키포인트를 감지하여 움직임이나 포즈를 추적합니다.
OBB(방향성 물체 감지): 방향 각도가 추가된 회전된 물체를 감지하여 정확도를 높입니다.

How do I use Ultralytics YOLO11 for object detection?

To use Ultralytics YOLO11 for object detection, follow these steps:

데이터 집합을 적절한 형식으로 준비합니다.
Train the YOLO11 model using the detection task.
모델을 사용하여 새 이미지 또는 비디오 프레임을 입력하여 예측을 수행합니다.

예

PythonCLI

from ultralytics import YOLO

# Load a pre-trained YOLO model (adjust model type as needed)
model = YOLO("yolo11n.pt")  # n, s, m, l, x versions available

# Perform object detection on an image
results = model.predict(source="image.jpg")  # Can also use video, directory, URL, etc.

# Display the results
results[0].show()  # Show the first image results

# Run YOLO detection from the command line
yolo detect model=yolo11n.pt source="image.jpg"  # Adjust model and source as needed

자세한 지침은 탐지 예시를 참조하세요.

What are the benefits of using YOLO11 for segmentation tasks?

Using YOLO11 for segmentation tasks provides several advantages:

높은 정확도: 세분화 작업은 U-Net 아키텍처의 변형을 활용하여 정밀한 세분화를 달성합니다.
Speed: YOLO11 is optimized for real-time applications, offering quick processing even for high-resolution images.
다양한 애플리케이션: 의료 영상, 자율 주행 및 세부적인 이미지 분할이 필요한 기타 애플리케이션에 이상적입니다.

Learn more about the benefits and use cases of YOLO11 for segmentation in the segmentation section.

Can Ultralytics YOLO11 handle pose estimation and keypoint detection?

Yes, Ultralytics YOLO11 can effectively perform pose estimation and keypoint detection with high accuracy and speed. This feature is particularly useful for tracking movements in sports analytics, healthcare, and human-computer interaction applications. YOLO11 detects keypoints in an image or video frame, allowing for precise pose estimation.

자세한 내용과 구현 팁은 포즈 추정 예시를 참조하세요.

Why should I choose Ultralytics YOLO11 for oriented object detection (OBB)?

Oriented Object Detection (OBB) with YOLO11 provides enhanced precision by detecting objects with an additional angle parameter. This feature is beneficial for applications requiring accurate localization of rotated objects, such as aerial imagery analysis and warehouse automation.