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.
Observa: Explore Ultralytics YOLO Tasks: Deteção de objectos, Segmentation, OBB, Tracking, and Pose Estimation.
Deteção
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.
Segmentação
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.
Classificação
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.
Pose
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.
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.
Conclusão
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.
FAQ
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:
- Deteção: Identifica e localiza objectos em imagens ou quadros de vídeo desenhando caixas delimitadoras à sua volta.
- Segmentação: Segmenta imagens em diferentes regiões com base no seu conteúdo, útil para aplicações como imagens médicas.
- Classificação: Categoriza imagens inteiras com base no seu conteúdo, aproveitando variantes da arquitetura EfficientNet.
- Estimativa de pose: Detecta pontos-chave específicos numa imagem ou num quadro de vídeo para seguir movimentos ou poses.
- Deteção de objectos orientados (OBB): Detecta objectos rodados com um ângulo de orientação adicional para maior precisão.
How do I use Ultralytics YOLO11 for object detection?
To use Ultralytics YOLO11 for object detection, follow these steps:
- Prepara o teu conjunto de dados no formato adequado.
- Train the YOLO11 model using the detection task.
- Utiliza o modelo para fazer previsões, introduzindo novas imagens ou fotogramas de vídeo.
Exemplo
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
Para obter instruções mais detalhadas, consulta os nossos exemplos de deteção.
What are the benefits of using YOLO11 for segmentation tasks?
Using YOLO11 for segmentation tasks provides several advantages:
- Alta precisão: A tarefa de segmentação utiliza uma variante da arquitetura U-Net para obter uma segmentação precisa.
- Speed: YOLO11 is optimized for real-time applications, offering quick processing even for high-resolution images.
- Aplicações múltiplas: É ideal para imagiologia médica, condução autónoma e outras aplicações que requerem uma segmentação detalhada da imagem.
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.
Para obter mais detalhes e dicas de implementação, visita os nossos exemplos de estimativa de pose.
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.
- Aumenta a precisão: O componente de ângulo reduz os falsos positivos para objectos rodados.
- Aplicações versáteis: Útil para tarefas de análise geoespacial, robótica, etc.
Consulta a secção Deteção de objectos orientados para obteres mais detalhes e exemplos.
