Ultralytics YOLO Hyperparameter Tuning Guide

Ultralytics YOLO Hyperparameter Tuning Guide#

Introduction#

Hyperparameter tuning is not just a one-time set-up but an iterative process aimed at optimizing the machine learning model’s performance metrics, such as accuracy, precision, and recall. In the context of Ultralytics YOLO, these hyperparameters could range from learning rate to architectural details, such as the number of layers or types of activation functions used.

What are Hyperparameters?#

Hyperparameters are high-level, structural settings for the algorithm. They are set prior to the training phase and remain constant during it. Here are some commonly tuned hyperparameters in Ultralytics YOLO:

Learning Rate lr0: Determines the step size at each iteration while moving towards a minimum in the loss function.
Batch Size batch: Number of images processed simultaneously in a forward pass.
Number of Epochs epochs: An epoch is one complete forward and backward pass of all the training examples.
Architecture Specifics: Such as channel counts, number of layers, types of activation functions, etc.

Hyperparameter Tuning Visual

For a full list of augmentation hyperparameters used in YOLO11 please refer to the configurations page.

Genetic Evolution and Mutation#

Ultralytics YOLO uses genetic algorithms to optimize hyperparameters. Genetic algorithms are inspired by the mechanism of natural selection and genetics.

Mutation: In the context of Ultralytics YOLO, mutation helps in locally searching the hyperparameter space by applying small, random changes to existing hyperparameters, producing new candidates for evaluation.
Crossover: Although crossover is a popular genetic algorithm technique, it is not currently used in Ultralytics YOLO for hyperparameter tuning. The focus is mainly on mutation for generating new hyperparameter sets.

Preparing for Hyperparameter Tuning#

Before you begin the tuning process, it’s important to:

Identify the Metrics: Determine the metrics you will use to evaluate the model’s performance. This could be AP50, F1-score, or others.
Set the Tuning Budget: Define how much computational resources you’re willing to allocate. Hyperparameter tuning can be computationally intensive.

Steps Involved#

Initialize Hyperparameters#

Start with a reasonable set of initial hyperparameters. This could either be the default hyperparameters set by Ultralytics YOLO or something based on your domain knowledge or previous experiments.

Mutate Hyperparameters#

Use the _mutate method to produce a new set of hyperparameters based on the existing set.

Train Model#

Training is performed using the mutated set of hyperparameters. The training performance is then assessed.

Evaluate Model#

Use metrics like AP50, F1-score, or custom metrics to evaluate the model’s performance.

Log Results#

It’s crucial to log both the performance metrics and the corresponding hyperparameters for future reference.

Repeat#

The process is repeated until either the set number of iterations is reached or the performance metric is satisfactory.

Usage Example#

Here’s how to use the model.tune() method to utilize the Tuner class for hyperparameter tuning of YOLO11n on COCO8 for 30 epochs with an AdamW optimizer and skipping plotting, checkpointing and validation other than on final epoch for faster Tuning.

!!! example

=== "Python"

    ```python
    from ultralytics import YOLO

    # Initialize the YOLO model
    model = YOLO("yolo11n.pt")

    # Tune hyperparameters on COCO8 for 30 epochs
    model.tune(data="coco8.yaml", epochs=30, iterations=300, optimizer="AdamW", plots=False, save=False, val=False)
    ```

Results#

After you’ve successfully completed the hyperparameter tuning process, you will obtain several files and directories that encapsulate the results of the tuning. The following describes each:

File Structure#

Here’s what the directory structure of the results will look like. Training directories like train1/ contain individual tuning iterations, i.e. one model trained with one set of hyperparameters. The tune/ directory contains tuning results from all the individual model trainings:

runs/
└── detect/
    ├── train1/
    ├── train2/
    ├── ...
    └── tune/
        ├── best_hyperparameters.yaml
        ├── best_fitness.png
        ├── tune_results.csv
        ├── tune_scatter_plots.png
        └── weights/
            ├── last.pt
            └── best.pt

File Descriptions#

best_hyperparameters.yaml#

This YAML file contains the best-performing hyperparameters found during the tuning process. You can use this file to initialize future trainings with these optimized settings.

Format: YAML
Usage: Hyperparameter results

Example:

# 558/900 iterations complete ✅ (45536.81s)
# Results saved to /usr/src/ultralytics/runs/detect/tune
# Best fitness=0.64297 observed at iteration 498
# Best fitness metrics are {'metrics/precision(B)': 0.87247, 'metrics/recall(B)': 0.71387, 'metrics/mAP50(B)': 0.79106, 'metrics/mAP50-95(B)': 0.62651, 'val/box_loss': 2.79884, 'val/cls_loss': 2.72386, 'val/dfl_loss': 0.68503, 'fitness': 0.64297}
# Best fitness model is /usr/src/ultralytics/runs/detect/train498
# Best fitness hyperparameters are printed below.

lr0: 0.00269
lrf: 0.00288
momentum: 0.73375
weight_decay: 0.00015
warmup_epochs: 1.22935
warmup_momentum: 0.1525
box: 18.27875
cls: 1.32899
dfl: 0.56016
hsv_h: 0.01148
hsv_s: 0.53554
hsv_v: 0.13636
degrees: 0.0
translate: 0.12431
scale: 0.07643
shear: 0.0
perspective: 0.0
flipud: 0.0
fliplr: 0.08631
mosaic: 0.42551
mixup: 0.0
copy_paste: 0.0

best_fitness.png#

This is a plot displaying fitness (typically a performance metric like AP50) against the number of iterations. It helps you visualize how well the genetic algorithm performed over time.

Format: PNG
Usage: Performance visualization

Hyperparameter Tuning Fitness vs Iteration

tune_results.csv#

A CSV file containing detailed results of each iteration during the tuning. Each row in the file represents one iteration, and it includes metrics like fitness score, precision, recall, as well as the hyperparameters used.

Format: CSV
Usage: Per-iteration results tracking.

Example:

  fitness,lr0,lrf,momentum,weight_decay,warmup_epochs,warmup_momentum,box,cls,dfl,hsv_h,hsv_s,hsv_v,degrees,translate,scale,shear,perspective,flipud,fliplr,mosaic,mixup,copy_paste
05021,0.01,0.01,0.937,0.0005,3.0,0.8,7.5,0.5,1.5,0.015,0.7,0.4,0.0,0.1,0.5,0.0,0.0,0.0,0.5,1.0,0.0,0.0
07217,0.01003,0.00967,0.93897,0.00049,2.79757,0.81075,7.5,0.50746,1.44826,0.01503,0.72948,0.40658,0.0,0.0987,0.4922,0.0,0.0,0.0,0.49729,1.0,0.0,0.0
06584,0.01003,0.00855,0.91009,0.00073,3.42176,0.95,8.64301,0.54594,1.72261,0.01503,0.59179,0.40658,0.0,0.0987,0.46955,0.0,0.0,0.0,0.49729,0.80187,0.0,0.0

tune_scatter_plots.png#

This file contains scatter plots generated from tune_results.csv, helping you visualize relationships between different hyperparameters and performance metrics. Note that hyperparameters initialized to 0 will not be tuned, such as degrees and shear below.

Format: PNG
Usage: Exploratory data analysis

Hyperparameter Tuning Scatter Plots

weights/#

This directory contains the saved PyTorch models for the last and the best iterations during the hyperparameter tuning process.

last.pt: The last.pt are the weights from the last epoch of training.
best.pt: The best.pt weights for the iteration that achieved the best fitness score.

Using these results, you can make more informed decisions for your future model trainings and analyses. Feel free to consult these artifacts to understand how well your model performed and how you might improve it further.

Conclusion#

The hyperparameter tuning process in Ultralytics YOLO is simplified yet powerful, thanks to its genetic algorithm-based approach focused on mutation. Following the steps outlined in this guide will assist you in systematically tuning your model to achieve better performance.

FAQ#

How do I optimize the learning rate for Ultralytics YOLO during hyperparameter tuning?#

To optimize the learning rate for Ultralytics YOLO, start by setting an initial learning rate using the lr0 parameter. Common values range from 0.001 to 0.01. During the hyperparameter tuning process, this value will be mutated to find the optimal setting. You can utilize the model.tune() method to automate this process. For example:

!!! example

=== "Python"

    ```python
    from ultralytics import YOLO

    # Initialize the YOLO model
    model = YOLO("yolo11n.pt")

    # Tune hyperparameters on COCO8 for 30 epochs
    model.tune(data="coco8.yaml", epochs=30, iterations=300, optimizer="AdamW", plots=False, save=False, val=False)
    ```

For more details, check the Ultralytics YOLO configuration page.

What are the benefits of using genetic algorithms for hyperparameter tuning in YOLO11?#

Genetic algorithms in Ultralytics YOLO11 provide a robust method for exploring the hyperparameter space, leading to highly optimized model performance. Key benefits include:

Efficient Search: Genetic algorithms like mutation can quickly explore a large set of hyperparameters.
Avoiding Local Minima: By introducing randomness, they help in avoiding local minima, ensuring better global optimization.
Performance Metrics: They adapt based on performance metrics such as AP50 and F1-score.

To see how genetic algorithms can optimize hyperparameters, check out the hyperparameter evolution guide.

How long does the hyperparameter tuning process take for Ultralytics YOLO?#

The time required for hyperparameter tuning with Ultralytics YOLO largely depends on several factors such as the size of the dataset, the complexity of the model architecture, the number of iterations, and the computational resources available. For instance, tuning YOLO11n on a dataset like COCO8 for 30 epochs might take several hours to days, depending on the hardware.

To effectively manage tuning time, define a clear tuning budget beforehand (internal section link). This helps in balancing resource allocation and optimization goals.

What metrics should I use to evaluate model performance during hyperparameter tuning in YOLO?#

When evaluating model performance during hyperparameter tuning in YOLO, you can use several key metrics:

AP50: The average precision at IoU threshold of 0.50.
F1-Score: The harmonic mean of precision and recall.
Precision and Recall: Individual metrics indicating the model’s accuracy in identifying true positives versus false positives and false negatives.

These metrics help you understand different aspects of your model’s performance. Refer to the Ultralytics YOLO performance metrics guide for a comprehensive overview.

Can I use Ultralytics HUB for hyperparameter tuning of YOLO models?#

Yes, you can use Ultralytics HUB for hyperparameter tuning of YOLO models. The HUB offers a no-code platform to easily upload datasets, train models, and perform hyperparameter tuning efficiently. It provides real-time tracking and visualization of tuning progress and results.

Explore more about using Ultralytics HUB for hyperparameter tuning in the Ultralytics HUB Cloud Training documentation.