Update YOLO11 Actions and Docs (#16596)
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---
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comments: true
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description: Learn how to enhance YOLOv8 experiment tracking and visualization with Weights & Biases for better model performance and management.
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keywords: YOLOv8, Weights & Biases, model training, experiment tracking, Ultralytics, machine learning, computer vision, model visualization
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description: Learn how to enhance YOLO11 experiment tracking and visualization with Weights & Biases for better model performance and management.
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keywords: YOLO11, Weights & Biases, model training, experiment tracking, Ultralytics, machine learning, computer vision, model visualization
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---
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# Enhancing YOLOv8 Experiment Tracking and Visualization with Weights & Biases
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# Enhancing YOLO11 Experiment Tracking and Visualization with Weights & Biases
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[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.
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[Object detection](https://www.ultralytics.com/glossary/object-detection) models like [Ultralytics YOLO11](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.
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<p align="center">
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<br>
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@ -16,10 +16,10 @@ keywords: YOLOv8, Weights & Biases, model training, experiment tracking, Ultraly
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allowfullscreen>
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</iframe>
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<br>
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<strong>Watch:</strong> How to use Ultralytics YOLOv8 with Weights and Biases
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<strong>Watch:</strong> How to use Ultralytics YOLO11 with Weights and Biases
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</p>
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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.
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This guide showcases Ultralytics YOLO11 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.
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## Weights & Biases
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@ -29,9 +29,9 @@ This guide showcases Ultralytics YOLOv8 integration with Weights & Biases' for e
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[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.
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## YOLOv8 Training with Weights & Biases
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## YOLO11 Training with Weights & Biases
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You can use Weights & Biases to bring efficiency and automation to your YOLOv8 training process.
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You can use Weights & Biases to bring efficiency and automation to your YOLO11 training process.
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## Installation
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@ -42,11 +42,11 @@ To install the required packages, run:
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=== "CLI"
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```bash
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# Install the required packages for YOLOv8 and Weights & Biases
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# Install the required packages for YOLO11 and Weights & Biases
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pip install --upgrade ultralytics==8.0.186 wandb
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```
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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.
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For detailed instructions and best practices related to the installation process, be sure to check our [YOLO11 Installation guide](../quickstart.md). While installing the required packages for YOLO11, if you encounter any difficulties, consult our [Common Issues guide](../guides/yolo-common-issues.md) for solutions and tips.
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## Configuring Weights & Biases
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@ -66,11 +66,11 @@ Start by initializing the Weights & Biases environment in your workspace. You ca
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Navigate to the Weights & Biases authorization page to create and retrieve your API key. Use this key to authenticate your environment with W&B.
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## Usage: Training YOLOv8 with Weights & Biases
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## Usage: Training YOLO11 with Weights & Biases
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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.
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Before diving into the usage instructions for YOLO11 model training with Weights & Biases, be sure to check out the range of [YOLO11 models offered by Ultralytics](../models/index.md). This will help you choose the most appropriate model for your project requirements.
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!!! example "Usage: Training YOLOv8 with Weights & Biases"
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!!! example "Usage: Training YOLO11 with Weights & Biases"
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=== "Python"
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@ -84,7 +84,7 @@ Before diving into the usage instructions for YOLOv8 model training with Weights
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wandb.init(project="ultralytics", job_type="training")
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# Load a YOLO model
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model = YOLO("yolov8n.pt")
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model = YOLO("yolo11n.pt")
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# Add W&B Callback for Ultralytics
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add_wandb_callback(model, enable_model_checkpointing=True)
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@ -108,7 +108,7 @@ Let's understand the steps showcased in the usage code snippet above.
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- **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.
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- **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.
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- **Step 2: Define the YOLO11 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.
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- **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.
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@ -132,13 +132,13 @@ Upon running the usage code snippet above, you can expect the following key outp
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### Viewing the Weights & Biases Dashboard
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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.
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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 YOLO11.
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## Key Features of the Weights & Biases Dashboard
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- **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).
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- **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.
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- **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 YOLO11.
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- **Comparative Analysis**: The platform allows side-by-side comparisons of different training runs, essential for assessing the impact of various model configurations.
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- **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).
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By using these features, you can effectively track, analyze, and optimize your YOLOv8 model's training, ensuring the best possible performance and efficiency.
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By using these features, you can effectively track, analyze, and optimize your YOLO11 model's training, ensuring the best possible performance and efficiency.
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## Summary
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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.
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This guide helped you explore Ultralytics' YOLO11 integration with Weights & Biases. It illustrates the ability of this integration to efficiently track and visualize model training and prediction results.
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For further details on usage, visit [Weights & Biases' official documentation](https://docs.wandb.ai/guides/integrations/ultralytics/).
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## FAQ
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### How do I install the required packages for YOLOv8 and Weights & Biases?
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### How do I install the required packages for YOLO11 and Weights & Biases?
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To install the required packages for YOLOv8 and Weights & Biases, open your command line interface and run:
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To install the required packages for YOLO11 and Weights & Biases, open your command line interface and run:
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```bash
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pip install --upgrade ultralytics==8.0.186 wandb
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```
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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.
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For further guidance on installation steps, refer to our [YOLO11 Installation guide](../quickstart.md). If you encounter issues, consult the [Common Issues guide](../guides/yolo-common-issues.md) for troubleshooting tips.
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### What are the benefits of integrating Ultralytics YOLOv8 with Weights & Biases?
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### What are the benefits of integrating Ultralytics YOLO11 with Weights & Biases?
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Integrating Ultralytics YOLOv8 with Weights & Biases offers several benefits including:
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Integrating Ultralytics YOLO11 with Weights & Biases offers several benefits including:
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- **Real-Time Metrics Tracking:** Observe metric changes during training for immediate insights.
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- **Hyperparameter Optimization:** Improve model performance by fine-tuning learning rate, [batch size](https://www.ultralytics.com/glossary/batch-size), etc.
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Explore these features in detail in the Weights & Biases Dashboard section above.
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### How can I configure Weights & Biases for YOLOv8 training?
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### How can I configure Weights & Biases for YOLO11 training?
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To configure Weights & Biases for YOLOv8 training, follow these steps:
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To configure Weights & Biases for YOLO11 training, follow these steps:
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1. Run the command to initialize Weights & Biases:
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```bash
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Detailed setup instructions can be found in the Configuring Weights & Biases section above.
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### How do I train a YOLOv8 model using Weights & Biases?
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### How do I train a YOLO11 model using Weights & Biases?
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For training a YOLOv8 model using Weights & Biases, use the following steps in a Python script:
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For training a YOLO11 model using Weights & Biases, use the following steps in a Python script:
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```python
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import wandb
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wandb.init(project="ultralytics", job_type="training")
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# Load a YOLO model
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model = YOLO("yolov8n.pt")
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model = YOLO("yolo11n.pt")
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# Add W&B Callback for Ultralytics
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add_wandb_callback(model, enable_model_checkpointing=True)
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This script initializes Weights & Biases, sets up the model, trains it, and logs results. For more details, visit the Usage section above.
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### Why should I use Ultralytics YOLOv8 with Weights & Biases over other platforms?
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### Why should I use Ultralytics YOLO11 with Weights & Biases over other platforms?
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Ultralytics YOLOv8 integrated with Weights & Biases offers several unique advantages:
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Ultralytics YOLO11 integrated with Weights & Biases offers several unique advantages:
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- **High Efficiency:** Real-time tracking of training metrics and performance optimization.
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- **Scalability:** Easily manage large-scale training jobs with robust resource monitoring and utilization tools.
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