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.

見るんだ： Explore Ultralytics YOLO Tasks: 物体検出, Segmentation, OBB, Tracking, and Pose Estimation.

検出

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.

検出例

セグメンテーション

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.

セグメンテーションの例

分類

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.

分類の例

ポーズ

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.

ポーズ例

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.

指向性検出

結論

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.

よくあるご質問

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:

検出：画像やビデオフレーム内のオブジェクトの周りにバウンディングボックスを描画することによって、オブジェクトを識別し、位置を特定すること。
セグメンテーション：画像をその内容に基づいて異なる領域にセグメンテーションし、医療画像などの用途に役立つ。
分類：EfficientNetアーキテクチャのバリエーションを活用し、画像全体をその内容に基づいて分類する。
ポーズ推定：動きやポーズを追跡するために、画像やビデオフレーム内の特定のキーポイントを検出すること。
オリエンテッドオブジェクト検出（OBB）：精度を向上させるために方向角度を追加して、回転したオブジェクトを検出します。

How do I use Ultralytics YOLO11 for object detection?

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

適切な形式でデータセットを準備する。
Train the YOLO11 model using the detection task.
新しい画像やビデオフレームを入力することで、モデルを使って予測を行う。

例

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

より詳細な手順については、検出例をご覧ください。

What are the benefits of using YOLO11 for segmentation tasks?

Using YOLO11 for segmentation tasks provides several advantages:

高精度：セグメンテーション・タスクは、正確なセグメンテーションを達成するために、U-Netアーキテクチャの変形を利用している。
Speed: YOLO11 is optimized for real-time applications, offering quick processing even for high-resolution images.
多様なアプリケーション医療用画像処理、自律走行、その他詳細な画像分割を必要とするアプリケーションに最適。

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.

詳細と実装のヒントについては、ポーズ推定例をご覧ください。

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.