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# Ultralytics YOLO11 Modes

## Introduction

Ultralytics YOLO11 is not just another object detection model; it's a versatile framework designed to cover the entire lifecycle of machine learning models—from data ingestion and model training to validation, deployment, and real-world tracking. Each mode serves a specific purpose and is engineered to offer you the flexibility and efficiency required for different tasks and use-cases.

Watch: Ultralytics Modes Tutorial: Train, Validate, Predict, Export & Benchmark.

### Modes at a Glance

Understanding the different modes that Ultralytics YOLO11 supports is critical to getting the most out of your models:

- Train mode: Fine-tune your model on custom or preloaded datasets.
- Val mode: A post-training checkpoint to validate model performance.
- Predict mode: Unleash the predictive power of your model on real-world data.
- Export mode: Make your model deployment-ready in various formats.
- Track mode: Extend your object detection model into real-time tracking applications.
- Benchmark mode: Analyze the speed and accuracy of your model in diverse deployment environments.

This comprehensive guide aims to give you an overview and practical insights into each mode, helping you harness the full potential of YOLO11.

## Train

Train mode is used for training a YOLO11 model on a custom dataset. In this mode, the model is trained using the specified dataset and hyperparameters. The training process involves optimizing the model's parameters so that it can accurately predict the classes and locations of objects in an image. Training is essential for creating models that can recognize specific objects relevant to your application.

Train Examples

## Val

Val mode is used for validating a YOLO11 model after it has been trained. In this mode, the model is evaluated on a validation set to measure its accuracy and generalization performance. Validation helps identify potential issues like overfitting and provides metrics such as mean Average Precision (mAP) to quantify model performance. This mode is crucial for tuning hyperparameters and improving overall model effectiveness.

Val Examples

## Predict

Predict mode is used for making predictions using a trained YOLO11 model on new images or videos. In this mode, the model is loaded from a checkpoint file, and the user can provide images or videos to perform inference. The model identifies and localizes objects in the input media, making it ready for real-world applications. Predict mode is the gateway to applying your trained model to solve practical problems.

Predict Examples

## Export

Export mode is used for converting a YOLO11 model to formats suitable for deployment across different platforms and devices. This mode transforms your PyTorch model into optimized formats like ONNX, TensorRT, or CoreML, enabling deployment in production environments. Exporting is essential for integrating your model with various software applications or hardware devices, often resulting in significant performance improvements.

Export Examples

## Track

Track mode extends YOLO11's object detection capabilities to track objects across video frames or live streams. This mode is particularly valuable for applications requiring persistent object identification, such as surveillance systems or self-driving cars. Track mode implements sophisticated algorithms like ByteTrack to maintain object identity across frames, even when objects temporarily disappear from view.

Track Examples

## Benchmark

Benchmark mode profiles the speed and accuracy of various export formats for YOLO11. This mode provides comprehensive metrics on model size, accuracy (mAP50-95 for detection tasks or accuracy_top5 for classification), and inference time across different formats like ONNX, OpenVINO, and TensorRT. Benchmarking helps you select the optimal export format based on your specific requirements for speed and accuracy in your deployment environment.

Benchmark Examples

## FAQ

### How do I train a custom object detection model with Ultralytics YOLO11?

Training a custom object detection model with Ultralytics YOLO11 involves using the train mode. You need a dataset formatted in YOLO format, containing images and corresponding annotation files. Use the following command to start the training process:

Example

Python CLI

    from ultralytics import YOLO

    # Load a pre-trained YOLO model (you can choose n, s, m, l, or x versions)
    model = YOLO("yolo11n.pt")

    # Start training on your custom dataset
    model.train(data="path/to/dataset.yaml", epochs=100, imgsz=640)

    # Train a YOLO model from the command line
    yolo train data=path/to/dataset.yaml epochs=100 imgsz=640

For more detailed instructions, you can refer to the Ultralytics Train Guide.

### What metrics does Ultralytics YOLO11 use to validate the model's performance?

Ultralytics YOLO11 uses various metrics during the validation process to assess model performance. These include:

- mAP (mean Average Precision): This evaluates the accuracy of object detection.
- IOU (Intersection over Union): Measures the overlap between predicted and ground truth bounding boxes.
- Precision and Recall: Precision measures the ratio of true positive detections to the total detected positives, while recall measures the ratio of true positive detections to the total actual positives.

You can run the following command to start the validation:

Example

Python CLI

    from ultralytics import YOLO

    # Load a pre-trained or custom YOLO model
    model = YOLO("yolo11n.pt")

    # Run validation on your dataset
    model.val(data="path/to/validation.yaml")

    # Validate a YOLO model from the command line
    yolo val data=path/to/validation.yaml

Refer to the Validation Guide for further details.

### How can I export my YOLO11 model for deployment?

Ultralytics YOLO11 offers export functionality to convert your trained model into various deployment formats such as ONNX, TensorRT, CoreML, and more. Use the following example to export your model:

Example

Python CLI

    from ultralytics import YOLO

    # Load your trained YOLO model
    model = YOLO("yolo11n.pt")

    # Export the model to ONNX format (you can specify other formats as needed)
    model.export(format="onnx")

    # Export a YOLO model to ONNX format from the command line
    yolo export model=yolo11n.pt format=onnx

Detailed steps for each export format can be found in the Export Guide.

### What is the purpose of the benchmark mode in Ultralytics YOLO11?

Benchmark mode in Ultralytics YOLO11 is used to analyze the speed and accuracy of various export formats such as ONNX, TensorRT, and OpenVINO. It provides metrics like model size, `mAP50-95` for object detection, and inference time across different hardware setups, helping you choose the most suitable format for your deployment needs.

Example

Python CLI

    from ultralytics.utils.benchmarks import benchmark

    # Run benchmark on GPU (device 0)
    # You can adjust parameters like model, dataset, image size, and precision as needed
    benchmark(model="yolo11n.pt", data="coco8.yaml", imgsz=640, half=False, device=0)

    # Benchmark a YOLO model from the command line
    # Adjust parameters as needed for your specific use case
    yolo benchmark model=yolo11n.pt data='coco8.yaml' imgsz=640 half=False device=0

For more details, refer to the Benchmark Guide.

### How can I perform real-time object tracking using Ultralytics YOLO11?

Real-time object tracking can be achieved using the track mode in Ultralytics YOLO11. This mode extends object detection capabilities to track objects across video frames or live feeds. Use the following example to enable tracking:

Example

Python CLI

    from ultralytics import YOLO

    # Load a pre-trained YOLO model
    model = YOLO("yolo11n.pt")

    # Start tracking objects in a video
    # You can also use live video streams or webcam input
    model.track(source="path/to/video.mp4")

    # Perform object tracking on a video from the command line
    # You can specify different sources like webcam (0) or RTSP streams
    yolo track source=path/to/video.mp4

For in-depth instructions, visit the Track Guide.

📅 Created 1 year ago ✏️ Updated 5 months ago

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