ultralytics/docs/en/integrations/ray-tune.md

comments	description	keywords
true	Optimize YOLO11 model performance with Ray Tune. Learn efficient hyperparameter tuning using advanced search strategies, parallelism, and early stopping.	YOLO11, Ray Tune, hyperparameter tuning, model optimization, machine learning, deep learning, AI, Ultralytics, Weights & Biases

Efficient Hyperparameter Tuning with Ray Tune and YOLO11

Hyperparameter tuning is vital in achieving peak model performance by discovering the optimal set of hyperparameters. This involves running trials with different hyperparameters and evaluating each trial's performance.

Accelerate Tuning with Ultralytics YOLO11 and Ray Tune

Ultralytics YOLO11 incorporates Ray Tune for hyperparameter tuning, streamlining the optimization of YOLO11 model hyperparameters. With Ray Tune, you can utilize advanced search strategies, parallelism, and early stopping to expedite the tuning process.

Ray Tune

Ray Tune is a hyperparameter tuning library designed for efficiency and flexibility. It supports various search strategies, parallelism, and early stopping strategies, and seamlessly integrates with popular machine learning frameworks, including Ultralytics YOLO11.

Integration with Weights & Biases

YOLO11 also allows optional integration with Weights & Biases for monitoring the tuning process.

Installation

To install the required packages, run:

!!! tip "Installation"

=== "CLI"

    ```bash
    # Install and update Ultralytics and Ray Tune packages
    pip install -U ultralytics "ray[tune]"

    # Optionally install W&B for logging
    pip install wandb
    ```

Usage

!!! example "Usage"

=== "Python"

    ```python
    from ultralytics import YOLO

    # Load a YOLO11n model
    model = YOLO("yolo11n.pt")

    # Start tuning hyperparameters for YOLO11n training on the COCO8 dataset
    result_grid = model.tune(data="coco8.yaml", use_ray=True)
    ```

tune() Method Parameters

The tune() method in YOLO11 provides an easy-to-use interface for hyperparameter tuning with Ray Tune. It accepts several arguments that allow you to customize the tuning process. Below is a detailed explanation of each parameter:

Parameter	Type	Description	Default Value
data	str	The dataset configuration file (in YAML format) to run the tuner on. This file should specify the training and validation data paths, as well as other dataset-specific settings.
space	dict, optional	A dictionary defining the hyperparameter search space for Ray Tune. Each key corresponds to a hyperparameter name, and the value specifies the range of values to explore during tuning. If not provided, YOLO11 uses a default search space with various hyperparameters.
grace_period	int, optional	The grace period in epochs for the ASHA scheduler in Ray Tune. The scheduler will not terminate any trial before this number of epochs, allowing the model to have some minimum training before making a decision on early stopping.	10
gpu_per_trial	int, optional	The number of GPUs to allocate per trial during tuning. This helps manage GPU usage, particularly in multi-GPU environments. If not provided, the tuner will use all available GPUs.	None
iterations	int, optional	The maximum number of trials to run during tuning. This parameter helps control the total number of hyperparameter combinations tested, ensuring the tuning process does not run indefinitely.	10
**train_args	dict, optional	Additional arguments to pass to the train() method during tuning. These arguments can include settings like the number of training epochs, batch size, and other training-specific configurations.	{}

By customizing these parameters, you can fine-tune the hyperparameter optimization process to suit your specific needs and available computational resources.

Default Search Space Description

The following table lists the default search space parameters for hyperparameter tuning in YOLO11 with Ray Tune. Each parameter has a specific value range defined by tune.uniform().

Parameter	Range	Description
lr0	tune.uniform(1e-5, 1e-1)	Initial learning rate that controls the step size during optimization. Higher values speed up training but may cause instability.
lrf	tune.uniform(0.01, 1.0)	Final learning rate factor that determines how much the learning rate decreases by the end of training.
momentum	tune.uniform(0.6, 0.98)	Momentum factor for the optimizer that helps accelerate training and overcome local minima.
weight_decay	tune.uniform(0.0, 0.001)	Regularization parameter that prevents overfitting by penalizing large weight values.
warmup_epochs	tune.uniform(0.0, 5.0)	Number of epochs with gradually increasing learning rate to stabilize early training.
warmup_momentum	tune.uniform(0.0, 0.95)	Initial momentum value that gradually increases during the warmup period.
box	tune.uniform(0.02, 0.2)	Weight for the bounding box loss component, balancing localization accuracy in the model.
cls	tune.uniform(0.2, 4.0)	Weight for the classification loss component, balancing class prediction accuracy in the model.
hsv_h	tune.uniform(0.0, 0.1)	Hue augmentation range that introduces color variability to help the model generalize.
hsv_s	tune.uniform(0.0, 0.9)	Saturation augmentation range that varies color intensity to improve robustness.
hsv_v	tune.uniform(0.0, 0.9)	Value (brightness) augmentation range that helps the model perform under various lighting conditions.
degrees	tune.uniform(0.0, 45.0)	Rotation augmentation range in degrees, improving recognition of rotated objects.
translate	tune.uniform(0.0, 0.9)	Translation augmentation range that shifts images horizontally and vertically.
scale	tune.uniform(0.0, 0.9)	Scaling augmentation range that simulates objects at different distances.
shear	tune.uniform(0.0, 10.0)	Shear augmentation range in degrees, simulating perspective shifts.
perspective	tune.uniform(0.0, 0.001)	Perspective augmentation range that simulates 3D viewpoint changes.
flipud	tune.uniform(0.0, 1.0)	Vertical flip augmentation probability, increasing dataset diversity.
fliplr	tune.uniform(0.0, 1.0)	Horizontal flip augmentation probability, useful for symmetrical objects.
mosaic	tune.uniform(0.0, 1.0)	Mosaic augmentation probability that combines four images into one training sample.
mixup	tune.uniform(0.0, 1.0)	Mixup augmentation probability that blends two images and their labels together.
cutmix	tune.uniform(0.0, 1.0)	Cutmix augmentation probability that combines image regions while maintaining local features, improving detection of partially occluded objects.
copy_paste	tune.uniform(0.0, 1.0)	Copy-paste augmentation probability that transfers objects between images to increase instance diversity.

Custom Search Space Example

In this example, we demonstrate how to use a custom search space for hyperparameter tuning with Ray Tune and YOLO11. By providing a custom search space, you can focus the tuning process on specific hyperparameters of interest.

!!! example "Usage"

```python
from ray import tune

from ultralytics import YOLO

# Define a YOLO model
model = YOLO("yolo11n.pt")

# Run Ray Tune on the model
result_grid = model.tune(
    data="coco8.yaml",
    space={"lr0": tune.uniform(1e-5, 1e-1)},
    epochs=50,
    use_ray=True,
)
```

In the code snippet above, we create a YOLO model with the "yolo11n.pt" pretrained weights. Then, we call the tune() method, specifying the dataset configuration with "coco8.yaml". We provide a custom search space for the initial learning rate lr0 using a dictionary with the key "lr0" and the value tune.uniform(1e-5, 1e-1). Finally, we pass additional training arguments, such as the number of epochs directly to the tune method as epochs=50.

Resuming An Interrupted Hyperparameter Tuning Session With Ray Tune

You can resume an interrupted Ray Tune session by passing resume=True. You can optionally pass the directory name used by Ray Tune under runs/{task} to resume. Otherwise, it would resume the last interrupted session. You don't need to provide the iterations and space again, but you need to provide the rest of the training arguments again including data and epochs.

!!! example "Using resume=True with model.tune()"

```python
from ultralytics import YOLO

# Define a YOLO model
model = YOLO("yolo11n.pt")

# Resume previous run
results = model.tune(use_ray=True, data="coco8.yaml", epochs=50, resume=True)

# Resume Ray Tune run with name 'tune_exp_2'
results = model.tune(use_ray=True, data="coco8.yaml", epochs=50, name="tune_exp_2", resume=True)
```

Processing Ray Tune Results

After running a hyperparameter tuning experiment with Ray Tune, you might want to perform various analyses on the obtained results. This guide will take you through common workflows for processing and analyzing these results.

Loading Tune Experiment Results from a Directory

After running the tuning experiment with tuner.fit(), you can load the results from a directory. This is useful, especially if you're performing the analysis after the initial training script has exited.

experiment_path = f"{storage_path}/{exp_name}"
print(f"Loading results from {experiment_path}...")

restored_tuner = tune.Tuner.restore(experiment_path, trainable=train_mnist)
result_grid = restored_tuner.get_results()

Basic Experiment-Level Analysis

Get an overview of how trials performed. You can quickly check if there were any errors during the trials.

if result_grid.errors:
    print("One or more trials failed!")
else:
    print("No errors!")

Basic Trial-Level Analysis

Access individual trial hyperparameter configurations and the last reported metrics.

for i, result in enumerate(result_grid):
    print(f"Trial #{i}: Configuration: {result.config}, Last Reported Metrics: {result.metrics}")

Plotting the Entire History of Reported Metrics for a Trial

You can plot the history of reported metrics for each trial to see how the metrics evolved over time.

import matplotlib.pyplot as plt

for i, result in enumerate(result_grid):
    plt.plot(
        result.metrics_dataframe["training_iteration"],
        result.metrics_dataframe["mean_accuracy"],
        label=f"Trial {i}",
    )

plt.xlabel("Training Iterations")
plt.ylabel("Mean Accuracy")
plt.legend()
plt.show()

Summary

In this documentation, we covered common workflows to analyze the results of experiments run with Ray Tune using Ultralytics. The key steps include loading the experiment results from a directory, performing basic experiment-level and trial-level analysis and plotting metrics.

Explore further by looking into Ray Tune's Analyze Results docs page to get the most out of your hyperparameter tuning experiments.

FAQ

How do I tune the hyperparameters of my YOLO11 model using Ray Tune?

To tune the hyperparameters of your Ultralytics YOLO11 model using Ray Tune, follow these steps:

1. Install the required packages:

   pip install -U ultralytics "ray[tune]"
   pip install wandb # optional for logging
   

2. Load your YOLO11 model and start tuning:

   from ultralytics import YOLO
   
   # Load a YOLO11 model
   model = YOLO("yolo11n.pt")
   
   # Start tuning with the COCO8 dataset
   result_grid = model.tune(data="coco8.yaml", use_ray=True)
   

This utilizes Ray Tune's advanced search strategies and parallelism to efficiently optimize your model's hyperparameters. For more information, check out the Ray Tune documentation.

What are the default hyperparameters for YOLO11 tuning with Ray Tune?

Ultralytics YOLO11 uses the following default hyperparameters for tuning with Ray Tune:

Parameter	Value Range	Description
lr0	tune.uniform(1e-5, 1e-1)	Initial learning rate
lrf	tune.uniform(0.01, 1.0)	Final learning rate factor
momentum	tune.uniform(0.6, 0.98)	Momentum
weight_decay	tune.uniform(0.0, 0.001)	Weight decay
warmup_epochs	tune.uniform(0.0, 5.0)	Warmup epochs
box	tune.uniform(0.02, 0.2)	Box loss weight
cls	tune.uniform(0.2, 4.0)	Class loss weight
hsv_h	tune.uniform(0.0, 0.1)	Hue augmentation range
translate	tune.uniform(0.0, 0.9)	Translation augmentation range

These hyperparameters can be customized to suit your specific needs. For a complete list and more details, refer to the Hyperparameter Tuning guide.

How can I integrate Weights & Biases with my YOLO11 model tuning?

To integrate Weights & Biases (W&B) with your Ultralytics YOLO11 tuning process:

1. Install W&B:

   pip install wandb
   

2. Modify your tuning script:

   import wandb
   
   from ultralytics import YOLO
   
   wandb.init(project="YOLO-Tuning", entity="your-entity")
   
   # Load YOLO model
   model = YOLO("yolo11n.pt")
   
   # Tune hyperparameters
   result_grid = model.tune(data="coco8.yaml", use_ray=True)
   

This setup will allow you to monitor the tuning process, track hyperparameter configurations, and visualize results in W&B.

Why should I use Ray Tune for hyperparameter optimization with YOLO11?

Ray Tune offers numerous advantages for hyperparameter optimization:

• Advanced Search Strategies: Utilizes algorithms like Bayesian Optimization and HyperOpt for efficient parameter search.
• Parallelism: Supports parallel execution of multiple trials, significantly speeding up the tuning process.
• Early Stopping: Employs strategies like ASHA to terminate under-performing trials early, saving computational resources.

Ray Tune seamlessly integrates with Ultralytics YOLO11, providing an easy-to-use interface for tuning hyperparameters effectively. To get started, check out the Hyperparameter Tuning guide.

How can I define a custom search space for YOLO11 hyperparameter tuning?

To define a custom search space for your YOLO11 hyperparameter tuning with Ray Tune:

from ray import tune

from ultralytics import YOLO

model = YOLO("yolo11n.pt")
search_space = {"lr0": tune.uniform(1e-5, 1e-1), "momentum": tune.uniform(0.6, 0.98)}
result_grid = model.tune(data="coco8.yaml", space=search_space, use_ray=True)

This customizes the range of hyperparameters like initial learning rate and momentum to be explored during the tuning process. For advanced configurations, refer to the Custom Search Space Example section.