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: Detección de objetos, Segmentation, OBB, Tracking, and Pose Estimation.

Detección

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.

Ejemplos de detección

Segmentación

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.

Ejemplos de segmentación

Clasificación

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.

Ejemplos de clasificación

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.

Ejemplos de poses

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.

Detección Orientada

Conclusión

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.

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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:

• Detección: Identificar y localizar objetos en imágenes o fotogramas de vídeo dibujando cuadros delimitadores a su alrededor.
• Segmentación: Segmentación de imágenes en diferentes regiones en función de su contenido, útil para aplicaciones como las imágenes médicas.
• Clasificación: Categorización de imágenes enteras en función de su contenido, aprovechando variantes de la arquitectura EfficientNet.
• Estimación de la pose: Detección de puntos clave específicos en un fotograma de imagen o vídeo para rastrear movimientos o poses.
• Detección de Objetos Orientados (OBB): Detección de objetos girados con un ángulo de orientación añadido para mejorar la precisión.

How do I use Ultralytics YOLO11 for object detection?

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

1. Prepara tu conjunto de datos en el formato adecuado.
2. Train the YOLO11 model using the detection task.
3. Utiliza el modelo para hacer predicciones introduciendo nuevas imágenes o fotogramas de vídeo.

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

Para obtener instrucciones más detalladas, consulta nuestros ejemplos de detección.

What are the benefits of using YOLO11 for segmentation tasks?

Using YOLO11 for segmentation tasks provides several advantages:

1. Alta precisión: La tarea de segmentación aprovecha una variante de la arquitectura U-Net para lograr una segmentación precisa.
2. Speed: YOLO11 is optimized for real-time applications, offering quick processing even for high-resolution images.
3. Múltiples aplicaciones: Es ideal para imágenes médicas, conducción autónoma y otras aplicaciones que requieran una segmentación detallada de las imágenes.

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 más detalles y consejos de aplicación, visita nuestros ejemplos de estimación 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.

• Mayor precisión: El componente angular reduce los falsos positivos de los objetos girados.
• Aplicaciones versátiles: Útil para tareas de análisis geoespacial, robótica, etc.

Consulta la sección Detección de Objetos Orientados para obtener más detalles y ejemplos.

📅 Created 11 months ago ✏️ Updated 5 days ago

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