ultralytics/docs/en/modes/train.md

comments	description	keywords
true	Learn how to efficiently train object detection models using YOLO11 with comprehensive instructions on settings, augmentation, and hardware utilization.	Ultralytics, YOLO11, model training, deep learning, object detection, GPU training, dataset augmentation, hyperparameter tuning, model performance, apple silicon training

Model Training with Ultralytics YOLO

Introduction

Training a deep learning model involves feeding it data and adjusting its parameters so that it can make accurate predictions. Train mode in Ultralytics YOLO11 is engineered for effective and efficient training of object detection models, fully utilizing modern hardware capabilities. This guide aims to cover all the details you need to get started with training your own models using YOLO11's robust set of features.

Watch: How to Train a YOLO model on Your Custom Dataset in Google Colab.

Why Choose Ultralytics YOLO for Training?

Here are some compelling reasons to opt for YOLO11's Train mode:

• Efficiency: Make the most out of your hardware, whether you're on a single-GPU setup or scaling across multiple GPUs.
• Versatility: Train on custom datasets in addition to readily available ones like COCO, VOC, and ImageNet.
• User-Friendly: Simple yet powerful CLI and Python interfaces for a straightforward training experience.
• Hyperparameter Flexibility: A broad range of customizable hyperparameters to fine-tune model performance.

Key Features of Train Mode

The following are some notable features of YOLO11's Train mode:

• Automatic Dataset Download: Standard datasets like COCO, VOC, and ImageNet are downloaded automatically on first use.
• Multi-GPU Support: Scale your training efforts seamlessly across multiple GPUs to expedite the process.
• Hyperparameter Configuration: The option to modify hyperparameters through YAML configuration files or CLI arguments.
• Visualization and Monitoring: Real-time tracking of training metrics and visualization of the learning process for better insights.

!!! tip

* YOLO11 datasets like COCO, VOC, ImageNet and many others automatically download on first use, i.e. `yolo train data=coco.yaml`

Usage Examples

Train YOLO11n on the COCO8 dataset for 100 epochs at image size 640. The training device can be specified using the device argument. If no argument is passed GPU device=0 will be used if available, otherwise device='cpu' will be used. See Arguments section below for a full list of training arguments.

!!! example "Single-GPU and CPU Training Example"

Device is determined automatically. If a GPU is available then it will be used, otherwise training will start on CPU.

=== "Python"

    ```python
    from ultralytics import YOLO

    # Load a model
    model = YOLO("yolo11n.yaml")  # build a new model from YAML
    model = YOLO("yolo11n.pt")  # load a pretrained model (recommended for training)
    model = YOLO("yolo11n.yaml").load("yolo11n.pt")  # build from YAML and transfer weights

    # Train the model
    results = model.train(data="coco8.yaml", epochs=100, imgsz=640)
    ```

=== "CLI"

    ```bash
    # Build a new model from YAML and start training from scratch
    yolo detect train data=coco8.yaml model=yolo11n.yaml epochs=100 imgsz=640

    # Start training from a pretrained *.pt model
    yolo detect train data=coco8.yaml model=yolo11n.pt epochs=100 imgsz=640

    # Build a new model from YAML, transfer pretrained weights to it and start training
    yolo detect train data=coco8.yaml model=yolo11n.yaml pretrained=yolo11n.pt epochs=100 imgsz=640
    ```

Multi-GPU Training

Multi-GPU training allows for more efficient utilization of available hardware resources by distributing the training load across multiple GPUs. This feature is available through both the Python API and the command-line interface. To enable multi-GPU training, specify the GPU device IDs you wish to use.

!!! example "Multi-GPU Training Example"

To train with 2 GPUs, CUDA devices 0 and 1 use the following commands. Expand to additional GPUs as required.

=== "Python"

    ```python
    from ultralytics import YOLO

    # Load a model
    model = YOLO("yolo11n.pt")  # load a pretrained model (recommended for training)

    # Train the model with 2 GPUs
    results = model.train(data="coco8.yaml", epochs=100, imgsz=640, device=[0, 1])
    ```

=== "CLI"

    ```bash
    # Start training from a pretrained *.pt model using GPUs 0 and 1
    yolo detect train data=coco8.yaml model=yolo11n.pt epochs=100 imgsz=640 device=0,1
    ```

Apple Silicon MPS Training

With the support for Apple silicon chips integrated in the Ultralytics YOLO models, it's now possible to train your models on devices utilizing the powerful Metal Performance Shaders (MPS) framework. The MPS offers a high-performance way of executing computation and image processing tasks on Apple's custom silicon.

To enable training on Apple silicon chips, you should specify 'mps' as your device when initiating the training process. Below is an example of how you could do this in Python and via the command line:

!!! example "MPS Training Example"

=== "Python"

    ```python
    from ultralytics import YOLO

    # Load a model
    model = YOLO("yolo11n.pt")  # load a pretrained model (recommended for training)

    # Train the model with MPS
    results = model.train(data="coco8.yaml", epochs=100, imgsz=640, device="mps")
    ```

=== "CLI"

    ```bash
    # Start training from a pretrained *.pt model using MPS
    yolo detect train data=coco8.yaml model=yolo11n.pt epochs=100 imgsz=640 device=mps
    ```

While leveraging the computational power of the Apple silicon chips, this enables more efficient processing of the training tasks. For more detailed guidance and advanced configuration options, please refer to the PyTorch MPS documentation.

Resuming Interrupted Trainings

Resuming training from a previously saved state is a crucial feature when working with deep learning models. This can come in handy in various scenarios, like when the training process has been unexpectedly interrupted, or when you wish to continue training a model with new data or for more epochs.

When training is resumed, Ultralytics YOLO loads the weights from the last saved model and also restores the optimizer state, learning rate scheduler, and the epoch number. This allows you to continue the training process seamlessly from where it was left off.

You can easily resume training in Ultralytics YOLO by setting the resume argument to True when calling the train method, and specifying the path to the .pt file containing the partially trained model weights.

Below is an example of how to resume an interrupted training using Python and via the command line:

!!! example "Resume Training Example"

=== "Python"

    ```python
    from ultralytics import YOLO

    # Load a model
    model = YOLO("path/to/last.pt")  # load a partially trained model

    # Resume training
    results = model.train(resume=True)
    ```

=== "CLI"

    ```bash
    # Resume an interrupted training
    yolo train resume model=path/to/last.pt
    ```

By setting resume=True, the train function will continue training from where it left off, using the state stored in the 'path/to/last.pt' file. If the resume argument is omitted or set to False, the train function will start a new training session.

Remember that checkpoints are saved at the end of every epoch by default, or at fixed intervals using the save_period argument, so you must complete at least 1 epoch to resume a training run.

Train Settings

The training settings for YOLO models encompass various hyperparameters and configurations used during the training process. These settings influence the model's performance, speed, and accuracy. Key training settings include batch size, learning rate, momentum, and weight decay. Additionally, the choice of optimizer, loss function, and training dataset composition can impact the training process. Careful tuning and experimentation with these settings are crucial for optimizing performance.

{% include "macros/train-args.md" %}

!!! info "Note on Batch-size Settings"

The `batch` argument can be configured in three ways:

- **Fixed [Batch Size](https://www.ultralytics.com/glossary/batch-size)**: Set an integer value (e.g., `batch=16`), specifying the number of images per batch directly.
- **Auto Mode (60% GPU Memory)**: Use `batch=-1` to automatically adjust batch size for approximately 60% CUDA memory utilization.
- **Auto Mode with Utilization Fraction**: Set a fraction value (e.g., `batch=0.70`) to adjust batch size based on the specified fraction of GPU memory usage.

Augmentation Settings and Hyperparameters

Augmentation techniques are essential for improving the robustness and performance of YOLO models by introducing variability into the training data, helping the model generalize better to unseen data. The following table outlines the purpose and effect of each augmentation argument:

{% include "macros/augmentation-args.md" %}

These settings can be adjusted to meet the specific requirements of the dataset and task at hand. Experimenting with different values can help find the optimal augmentation strategy that leads to the best model performance.

!!! info

For more information about training augmentation operations, see the [reference section](../reference/data/augment.md).

Logging

In training a YOLO11 model, you might find it valuable to keep track of the model's performance over time. This is where logging comes into play. Ultralytics' YOLO provides support for three types of loggers - Comet, ClearML, and TensorBoard.

To use a logger, select it from the dropdown menu in the code snippet above and run it. The chosen logger will be installed and initialized.

Comet

Comet is a platform that allows data scientists and developers to track, compare, explain and optimize experiments and models. It provides functionalities such as real-time metrics, code diffs, and hyperparameters tracking.

To use Comet:

!!! example

=== "Python"

    ```python
    # pip install comet_ml
    import comet_ml

    comet_ml.init()
    ```

Remember to sign in to your Comet account on their website and get your API key. You will need to add this to your environment variables or your script to log your experiments.

ClearML

ClearML is an open-source platform that automates tracking of experiments and helps with efficient sharing of resources. It is designed to help teams manage, execute, and reproduce their ML work more efficiently.

To use ClearML:

!!! example

=== "Python"

    ```python
    # pip install clearml
    import clearml

    clearml.browser_login()
    ```

After running this script, you will need to sign in to your ClearML account on the browser and authenticate your session.

TensorBoard

TensorBoard is a visualization toolkit for TensorFlow. It allows you to visualize your TensorFlow graph, plot quantitative metrics about the execution of your graph, and show additional data like images that pass through it.

To use TensorBoard in Google Colab:

!!! example

=== "CLI"

    ```bash
    load_ext tensorboard
    tensorboard --logdir ultralytics/runs  # replace with 'runs' directory
    ```

To use TensorBoard locally run the below command and view results at http://localhost:6006/.

!!! example

=== "CLI"

    ```bash
    tensorboard --logdir ultralytics/runs  # replace with 'runs' directory
    ```

This will load TensorBoard and direct it to the directory where your training logs are saved.

After setting up your logger, you can then proceed with your model training. All training metrics will be automatically logged in your chosen platform, and you can access these logs to monitor your model's performance over time, compare different models, and identify areas for improvement.

FAQ

How do I train an object detection model using Ultralytics YOLO11?

To train an object detection model using Ultralytics YOLO11, you can either use the Python API or the CLI. Below is an example for both:

!!! example "Single-GPU and CPU Training Example"

=== "Python"

    ```python
    from ultralytics import YOLO

    # Load a model
    model = YOLO("yolo11n.pt")  # load a pretrained model (recommended for training)

    # Train the model
    results = model.train(data="coco8.yaml", epochs=100, imgsz=640)
    ```

=== "CLI"

    ```bash
    yolo detect train data=coco8.yaml model=yolo11n.pt epochs=100 imgsz=640
    ```

For more details, refer to the Train Settings section.

What are the key features of Ultralytics YOLO11's Train mode?

The key features of Ultralytics YOLO11's Train mode include:

• Automatic Dataset Download: Automatically downloads standard datasets like COCO, VOC, and ImageNet.
• Multi-GPU Support: Scale training across multiple GPUs for faster processing.
• Hyperparameter Configuration: Customize hyperparameters through YAML files or CLI arguments.
• Visualization and Monitoring: Real-time tracking of training metrics for better insights.

These features make training efficient and customizable to your needs. For more details, see the Key Features of Train Mode section.

How do I resume training from an interrupted session in Ultralytics YOLO11?

To resume training from an interrupted session, set the resume argument to True and specify the path to the last saved checkpoint.

!!! example "Resume Training Example"

=== "Python"

    ```python
    from ultralytics import YOLO

    # Load the partially trained model
    model = YOLO("path/to/last.pt")

    # Resume training
    results = model.train(resume=True)
    ```

=== "CLI"

    ```bash
    yolo train resume model=path/to/last.pt
    ```

Check the section on Resuming Interrupted Trainings for more information.

Can I train YOLO11 models on Apple silicon chips?

Yes, Ultralytics YOLO11 supports training on Apple silicon chips utilizing the Metal Performance Shaders (MPS) framework. Specify 'mps' as your training device.

!!! example "MPS Training Example"

=== "Python"

    ```python
    from ultralytics import YOLO

    # Load a pretrained model
    model = YOLO("yolo11n.pt")

    # Train the model on Apple silicon chip (M1/M2/M3/M4)
    results = model.train(data="coco8.yaml", epochs=100, imgsz=640, device="mps")
    ```

=== "CLI"

    ```bash
    yolo detect train data=coco8.yaml model=yolo11n.pt epochs=100 imgsz=640 device=mps
    ```

For more details, refer to the Apple Silicon MPS Training section.

What are the common training settings, and how do I configure them?

Ultralytics YOLO11 allows you to configure a variety of training settings such as batch size, learning rate, epochs, and more through arguments. Here's a brief overview:

Argument	Default	Description
model	None	Path to the model file for training.
data	None	Path to the dataset configuration file (e.g., coco8.yaml).
epochs	100	Total number of training epochs.
batch	16	Batch size, adjustable as integer or auto mode.
imgsz	640	Target image size for training.
device	None	Computational device(s) for training like cpu, 0, 0,1, or mps.
save	True	Enables saving of training checkpoints and final model weights.

For an in-depth guide on training settings, check the Train Settings section.