---
comments: true
description: Learn how to enhance YOLOv8 experiment tracking and visualization with Weights & Biases for better model performance and management.
keywords: YOLOv8, Weights & Biases, model training, experiment tracking, Ultralytics, machine learning, computer vision, model visualization
---

# Enhancing YOLOv8 Experiment Tracking and Visualization with Weights & Biases

[Object detection](https://www.ultralytics.com/glossary/object-detection) models like [Ultralytics YOLOv8](https://github.com/ultralytics/ultralytics) have become integral to many [computer vision](https://www.ultralytics.com/glossary/computer-vision-cv) applications. However, training, evaluating, and deploying these complex models introduces several challenges. Tracking key training metrics, comparing model variants, analyzing model behavior, and detecting issues require substantial instrumentation and experiment management.

<p align="center">
  <br>
  <iframe loading="lazy" width="720" height="405" src="https://www.youtube.com/embed/EeDd5P4eS6A"
    title="YouTube video player" frameborder="0"
    allow="accelerometer; autoplay; clipboard-write; encrypted-media; gyroscope; picture-in-picture; web-share"
    allowfullscreen>
  </iframe>
  <br>
  <strong>Watch:</strong> How to use Ultralytics YOLOv8 with Weights and Biases
</p>

This guide showcases Ultralytics YOLOv8 integration with Weights & Biases' for enhanced experiment tracking, model-checkpointing, and visualization of model performance. It also includes instructions for setting up the integration, training, fine-tuning, and visualizing results using Weights & Biases' interactive features.

## Weights & Biases

<p align="center">
  <img width="800" src="https://github.com/ultralytics/docs/releases/download/0/wandb-demo-experiments.avif" alt="Weights & Biases Overview">
</p>

[Weights & Biases](https://wandb.ai/site) is a cutting-edge MLOps platform designed for tracking, visualizing, and managing [machine learning](https://www.ultralytics.com/glossary/machine-learning-ml) experiments. It features automatic logging of training metrics for full experiment reproducibility, an interactive UI for streamlined data analysis, and efficient model management tools for deploying across various environments.

## YOLOv8 Training with Weights & Biases

You can use Weights & Biases to bring efficiency and automation to your YOLOv8 training process.

## Installation

To install the required packages, run:

!!! tip "Installation"

    === "CLI"

        ```bash
        # Install the required packages for YOLOv8 and Weights & Biases
        pip install --upgrade ultralytics==8.0.186 wandb
        ```

For detailed instructions and best practices related to the installation process, be sure to check our [YOLOv8 Installation guide](../quickstart.md). While installing the required packages for YOLOv8, if you encounter any difficulties, consult our [Common Issues guide](../guides/yolo-common-issues.md) for solutions and tips.

## Configuring Weights & Biases

After installing the necessary packages, the next step is to set up your Weights & Biases environment. This includes creating a Weights & Biases account and obtaining the necessary API key for a smooth connection between your development environment and the W&B platform.

Start by initializing the Weights & Biases environment in your workspace. You can do this by running the following command and following the prompted instructions.

!!! tip "Initial SDK Setup"

    === "CLI"

        ```bash
        # Initialize your Weights & Biases environment
        import wandb
        wandb.login()
        ```

Navigate to the Weights & Biases authorization page to create and retrieve your API key. Use this key to authenticate your environment with W&B.

## Usage: Training YOLOv8 with Weights & Biases

Before diving into the usage instructions for YOLOv8 model training with Weights & Biases, be sure to check out the range of [YOLOv8 models offered by Ultralytics](../models/index.md). This will help you choose the most appropriate model for your project requirements.

!!! example "Usage: Training YOLOv8 with Weights & Biases"

    === "Python"

        ```python
        import wandb
        from wandb.integration.ultralytics import add_wandb_callback

        from ultralytics import YOLO

        # Initialize a Weights & Biases run
        wandb.init(project="ultralytics", job_type="training")

        # Load a YOLO model
        model = YOLO("yolov8n.pt")

        # Add W&B Callback for Ultralytics
        add_wandb_callback(model, enable_model_checkpointing=True)

        # Train and Fine-Tune the Model
        model.train(project="ultralytics", data="coco8.yaml", epochs=5, imgsz=640)

        # Validate the Model
        model.val()

        # Perform Inference and Log Results
        model(["path/to/image1", "path/to/image2"])

        # Finalize the W&B Run
        wandb.finish()
        ```

### Understanding the Code

Let's understand the steps showcased in the usage code snippet above.

- **Step 1: Initialize a Weights & Biases Run**: Start by initializing a Weights & Biases run, specifying the project name and the job type. This run will track and manage the training and validation processes of your model.

- **Step 2: Define the YOLOv8 Model and Dataset**: Specify the model variant and the dataset you wish to use. The YOLO model is then initialized with the specified model file.

- **Step 3: Add Weights & Biases Callback for Ultralytics**: This step is crucial as it enables the automatic logging of training metrics and validation results to Weights & Biases, providing a detailed view of the model's performance.

- **Step 4: Train and Fine-Tune the Model**: Begin training the model with the specified dataset, number of epochs, and image size. The training process includes logging of metrics and predictions at the end of each [epoch](https://www.ultralytics.com/glossary/epoch), offering a comprehensive view of the model's learning progress.

- **Step 5: Validate the Model**: After training, the model is validated. This step is crucial for assessing the model's performance on unseen data and ensuring its generalizability.

- **Step 6: Perform Inference and Log Results**: The model performs predictions on specified images. These predictions, along with visual overlays and insights, are automatically logged in a W&B Table for interactive exploration.

- **Step 7: Finalize the W&B Run**: This step marks the end of data logging and saves the final state of your model's training and validation process in the W&B dashboard.

### Understanding the Output

Upon running the usage code snippet above, you can expect the following key outputs:

- The setup of a new run with its unique ID, indicating the start of the training process.
- A concise summary of the model's structure, including the number of layers and parameters.
- Regular updates on important metrics such as box loss, cls loss, dfl loss, [precision](https://www.ultralytics.com/glossary/precision), [recall](https://www.ultralytics.com/glossary/recall), and mAP scores during each training epoch.
- At the end of training, detailed metrics including the model's inference speed, and overall [accuracy](https://www.ultralytics.com/glossary/accuracy) metrics are displayed.
- Links to the Weights & Biases dashboard for in-depth analysis and visualization of the training process, along with information on local log file locations.

### Viewing the Weights & Biases Dashboard

After running the usage code snippet, you can access the Weights & Biases (W&B) dashboard through the provided link in the output. This dashboard offers a comprehensive view of your model's training process with YOLOv8.

## Key Features of the Weights & Biases Dashboard

- **Real-Time Metrics Tracking**: Observe metrics like loss, accuracy, and validation scores as they evolve during the training, offering immediate insights for model tuning. [See how experiments are tracked using Weights & Biases](https://imgur.com/D6NVnmN).

- **Hyperparameter Optimization**: Weights & Biases aids in fine-tuning critical parameters such as [learning rate](https://www.ultralytics.com/glossary/learning-rate), batch size, and more, enhancing the performance of YOLOv8.

- **Comparative Analysis**: The platform allows side-by-side comparisons of different training runs, essential for assessing the impact of various model configurations.

- **Visualization of Training Progress**: Graphical representations of key metrics provide an intuitive understanding of the model's performance across epochs. [See how Weights & Biases helps you visualize validation results](https://imgur.com/a/kU5h7W4).

- **Resource Monitoring**: Keep track of CPU, GPU, and memory usage to optimize the efficiency of the training process.

- **Model Artifacts Management**: Access and share model checkpoints, facilitating easy deployment and collaboration.

- **Viewing Inference Results with Image Overlay**: Visualize the prediction results on images using interactive overlays in Weights & Biases, providing a clear and detailed view of model performance on real-world data. For more detailed information on Weights & Biases' image overlay capabilities, check out this [link](https://docs.wandb.ai/guides/track/log/media#image-overlays). [See how Weights & Biases' image overlays helps visualize model inferences](https://imgur.com/a/UTSiufs).

By using these features, you can effectively track, analyze, and optimize your YOLOv8 model's training, ensuring the best possible performance and efficiency.

## Summary

This guide helped you explore Ultralytics' YOLOv8 integration with Weights & Biases. It illustrates the ability of this integration to efficiently track and visualize model training and prediction results.

For further details on usage, visit [Weights & Biases' official documentation](https://docs.wandb.ai/guides/integrations/ultralytics).

Also, be sure to check out the [Ultralytics integration guide page](../integrations/index.md), to learn more about different exciting integrations.

## FAQ

### How do I install the required packages for YOLOv8 and Weights & Biases?

To install the required packages for YOLOv8 and Weights & Biases, open your command line interface and run:

```bash
pip install --upgrade ultralytics==8.0.186 wandb
```

For further guidance on installation steps, refer to our [YOLOv8 Installation guide](../quickstart.md). If you encounter issues, consult the [Common Issues guide](../guides/yolo-common-issues.md) for troubleshooting tips.

### What are the benefits of integrating Ultralytics YOLOv8 with Weights & Biases?

Integrating Ultralytics YOLOv8 with Weights & Biases offers several benefits including:

- **Real-Time Metrics Tracking:** Observe metric changes during training for immediate insights.
- **Hyperparameter Optimization:** Improve model performance by fine-tuning learning rate, [batch size](https://www.ultralytics.com/glossary/batch-size), etc.
- **Comparative Analysis:** Side-by-side comparison of different training runs.
- **Resource Monitoring:** Keep track of CPU, GPU, and memory usage.
- **Model Artifacts Management:** Easy access and sharing of model checkpoints.

Explore these features in detail in the Weights & Biases Dashboard section above.

### How can I configure Weights & Biases for YOLOv8 training?

To configure Weights & Biases for YOLOv8 training, follow these steps:

1. Run the command to initialize Weights & Biases:
    ```bash
    import wandb
    wandb.login()
    ```
2. Retrieve your API key from the Weights & Biases website.
3. Use the API key to authenticate your development environment.

Detailed setup instructions can be found in the Configuring Weights & Biases section above.

### How do I train a YOLOv8 model using Weights & Biases?

For training a YOLOv8 model using Weights & Biases, use the following steps in a Python script:

```python
import wandb
from wandb.integration.ultralytics import add_wandb_callback

from ultralytics import YOLO

# Initialize a Weights & Biases run
wandb.init(project="ultralytics", job_type="training")

# Load a YOLO model
model = YOLO("yolov8n.pt")

# Add W&B Callback for Ultralytics
add_wandb_callback(model, enable_model_checkpointing=True)

# Train and Fine-Tune the Model
model.train(project="ultralytics", data="coco8.yaml", epochs=5, imgsz=640)

# Validate the Model
model.val()

# Perform Inference and Log Results
model(["path/to/image1", "path/to/image2"])

# Finalize the W&B Run
wandb.finish()
```

This script initializes Weights & Biases, sets up the model, trains it, and logs results. For more details, visit the Usage section above.

### Why should I use Ultralytics YOLOv8 with Weights & Biases over other platforms?

Ultralytics YOLOv8 integrated with Weights & Biases offers several unique advantages:

- **High Efficiency:** Real-time tracking of training metrics and performance optimization.
- **Scalability:** Easily manage large-scale training jobs with robust resource monitoring and utilization tools.
- **Interactivity:** A user-friendly interactive UI for [data visualization](https://www.ultralytics.com/glossary/data-visualization) and model management.
- **Community and Support:** Strong integration documentation and community support with flexible customization and enhancement options.

For comparisons with other platforms like Comet and ClearML, refer to [Ultralytics integrations](../integrations/index.md).
