diff --git a/docs/en/datasets/pose/hand-keypoints.md b/docs/en/datasets/pose/hand-keypoints.md index 86548a02..dd3c19b1 100644 --- a/docs/en/datasets/pose/hand-keypoints.md +++ b/docs/en/datasets/pose/hand-keypoints.md @@ -8,7 +8,7 @@ keywords: Hand KeyPoints, pose estimation, dataset, keypoints, MediaPipe, YOLO, ## Introduction -The hand-keypoints dataset contains 26,768 images of hands annotated with keypoints, making it suitable for training models like Ultralytics YOLO for pose estimation tasks. The annotations were generated using the Google MediaPipe library, ensuring high accuracy and consistency, and the dataset is compatible [Ultralytics YOLO11](https://github.com/ultralytics/ultralytics) formats. +The hand-keypoints dataset contains 26,768 images of hands annotated with keypoints, making it suitable for training models like Ultralytics YOLO for pose estimation tasks. The annotations were generated using the Google MediaPipe library, ensuring high [accuracy](https://www.ultralytics.com/glossary/accuracy) and consistency, and the dataset is compatible [Ultralytics YOLO11](https://github.com/ultralytics/ultralytics) formats. ## Hand Landmarks diff --git a/docs/en/guides/security-alarm-system.md b/docs/en/guides/security-alarm-system.md index a9523dd6..e8562485 100644 --- a/docs/en/guides/security-alarm-system.md +++ b/docs/en/guides/security-alarm-system.md @@ -8,7 +8,7 @@ keywords: YOLO11, Security Alarm System, real-time object detection, Ultralytics Security Alarm System -The Security Alarm System Project utilizing Ultralytics YOLO11 integrates advanced [computer vision](https://www.ultralytics.com/glossary/computer-vision-cv) capabilities to enhance security measures. YOLO11, developed by Ultralytics, provides real-time object detection, allowing the system to identify and respond to potential security threats promptly. This project offers several advantages: +The Security Alarm System Project utilizing Ultralytics YOLO11 integrates advanced [computer vision](https://www.ultralytics.com/glossary/computer-vision-cv) capabilities to enhance security measures. YOLO11, developed by Ultralytics, provides real-time [object detection](https://www.ultralytics.com/glossary/object-detection), allowing the system to identify and respond to potential security threats promptly. This project offers several advantages: - **Real-time Detection:** YOLO11's efficiency enables the Security Alarm System to detect and respond to security incidents in real-time, minimizing response time. - **[Accuracy](https://www.ultralytics.com/glossary/accuracy):** YOLO11 is known for its accuracy in object detection, reducing false positives and enhancing the reliability of the security alarm system. diff --git a/docs/en/guides/steps-of-a-cv-project.md b/docs/en/guides/steps-of-a-cv-project.md index b0f03c1e..ca067547 100644 --- a/docs/en/guides/steps-of-a-cv-project.md +++ b/docs/en/guides/steps-of-a-cv-project.md @@ -147,7 +147,7 @@ It's important to keep in mind that proper dataset management is vital for effic It's important to assess your model's performance using various metrics and refine it to improve [accuracy](https://www.ultralytics.com/glossary/accuracy). [Evaluating](../modes/val.md) helps identify areas where the model excels and where it may need improvement. Fine-tuning ensures the model is optimized for the best possible performance. -- **[Performance Metrics](./yolo-performance-metrics.md):** Use metrics like accuracy, [precision](https://www.ultralytics.com/glossary/precision), recall, and F1-score to evaluate your model's performance. These metrics provide insights into how well your model is making predictions. +- **[Performance Metrics](./yolo-performance-metrics.md):** Use metrics like accuracy, [precision](https://www.ultralytics.com/glossary/precision), [recall](https://www.ultralytics.com/glossary/recall), and F1-score to evaluate your model's performance. These metrics provide insights into how well your model is making predictions. - **[Hyperparameter Tuning](./hyperparameter-tuning.md):** Adjust hyperparameters to optimize model performance. Techniques like grid search or random search can help find the best hyperparameter values. - Fine-Tuning: Make small adjustments to the model architecture or training process to enhance performance. This might involve tweaking [learning rates](https://www.ultralytics.com/glossary/learning-rate), [batch sizes](https://www.ultralytics.com/glossary/batch-size), or other model parameters. diff --git a/docs/en/integrations/weights-biases.md b/docs/en/integrations/weights-biases.md index 9f2cbb2f..06e071bf 100644 --- a/docs/en/integrations/weights-biases.md +++ b/docs/en/integrations/weights-biases.md @@ -6,7 +6,7 @@ keywords: YOLO11, Weights & Biases, model training, experiment tracking, Ultraly # Enhancing YOLO11 Experiment Tracking and Visualization with Weights & Biases -[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. +[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 introduce several challenges. Tracking key training metrics, comparing model variants, analyzing model behavior, and detecting issues require significant instrumentation and experiment management.


@@ -19,7 +19,7 @@ keywords: YOLO11, Weights & Biases, model training, experiment tracking, Ultraly Watch: How to use Ultralytics YOLO11 with Weights and Biases

-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. +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. ## Weights & Biases @@ -42,8 +42,8 @@ To install the required packages, run: === "CLI" ```bash - # Install the required packages for YOLO11 and Weights & Biases - pip install --upgrade ultralytics==8.0.186 wandb + # Install the required packages for Ultralytics YOLO and Weights & Biases + pip install -U ultralytics wandb ``` 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. @@ -56,12 +56,20 @@ Start by initializing the Weights & Biases environment in your workspace. You ca !!! tip "Initial SDK Setup" + === "Python" + + ```python + import wandb + + # Initialize your Weights & Biases environment + wandb.login(key="") + ``` + === "CLI" ```bash # Initialize your Weights & Biases environment - import wandb - wandb.login() + 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. @@ -75,50 +83,39 @@ Before diving into the usage instructions for YOLO11 model training with Weights === "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("yolo11n.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() + model.train(data="coco8.yaml", epochs=5, project="ultralytics", name="yolo11n") ``` -### Understanding the Code + === "CLI" -Let's understand the steps showcased in the usage code snippet above. + ```bash + # Train a YOLO11 model with Weights & Biases + yolo train data=coco8.yaml epochs=5 project=ultralytics name=yolo11n + ``` -- **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. +### W&B Arguments -- **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. +| Argument | Default | Description | +| -------- | ------- | ------------------------------------------------------------------------------------------------------------------ | +| project | `None` | Specifies the name of the project logged locally and in W&B. This way you can group multiple runs together. | +| name | `None` | The name of the training run. This determines the name used to create subfolders and the name used for W&B logging | -- **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. +!!! Tip "Enable or Disable Weights & Biases" +If you want to enable or disable Weights & Biases logging, you can use the `wandb` command. By default, Weights & Biases logging is enabled. -- **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. + ```bash + # Enable Weights & Biases logging + wandb enabled -- **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. + # Disable Weights & Biases logging + wandb disabled + ``` ### Understanding the Output @@ -126,7 +123,7 @@ Upon running the usage code snippet above, you can expect the following key outp - 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. +- 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](https://www.ultralytics.com/glossary/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. @@ -138,7 +135,7 @@ After running the usage code snippet, you can access the Weights & Biases (W&B) - **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 YOLO11. +- **Hyperparameter Optimization**: Weights & Biases aids in fine-tuning critical parameters such as [learning rate](https://www.ultralytics.com/glossary/learning-rate), [batch size](https://www.ultralytics.com/glossary/batch-size), and more, enhancing the performance of YOLO11. - **Comparative Analysis**: The platform allows side-by-side comparisons of different training runs, essential for assessing the impact of various model configurations. @@ -154,7 +151,7 @@ By using these features, you can effectively track, analyze, and optimize your Y ## Summary -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. +This guide helped you explore the Ultralytics YOLO 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/). @@ -162,83 +159,83 @@ Also, be sure to check out the [Ultralytics integration guide page](../integrati ## FAQ -### How do I install the required packages for YOLO11 and Weights & Biases? +### How do I integrate Weights & Biases with Ultralytics YOLO11? -To install the required packages for YOLO11 and Weights & Biases, open your command line interface and run: +To integrate Weights & Biases with Ultralytics YOLO11: + +1. Install the required packages: ```bash -pip install --upgrade ultralytics==8.0.186 wandb +pip install -U ultralytics wandb ``` -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. - -### What are the benefits of integrating Ultralytics YOLO11 with Weights & Biases? - -Integrating Ultralytics YOLO11 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 YOLO11 training? - -To configure Weights & Biases for YOLO11 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 YOLO11 model using Weights & Biases? - -For training a YOLO11 model using Weights & Biases, use the following steps in a Python script: +2. Log in to your Weights & Biases account: ```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("yolo11n.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() +wandb.login(key="") ``` -This script initializes Weights & Biases, sets up the model, trains it, and logs results. For more details, visit the Usage section above. +3. Train your YOLO11 model with W&B logging enabled: -### Why should I use Ultralytics YOLO11 with Weights & Biases over other platforms? +```python +from ultralytics import YOLO -Ultralytics YOLO11 integrated with Weights & Biases offers several unique advantages: +model = YOLO("yolo11n.pt") +model.train(data="coco8.yaml", epochs=5, project="ultralytics", name="yolo11n") +``` -- **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. +This will automatically log metrics, hyperparameters, and model artifacts to your W&B project. -For comparisons with other platforms like Comet and ClearML, refer to [Ultralytics integrations](../integrations/index.md). +### What are the key features of Weights & Biases integration with YOLO11? + +The key features include: + +- Real-time metrics tracking during training +- Hyperparameter optimization tools +- Comparative analysis of different training runs +- Visualization of training progress through graphs +- Resource monitoring (CPU, GPU, memory usage) +- Model artifacts management and sharing +- Viewing inference results with image overlays + +These features help in tracking experiments, optimizing models, and collaborating more effectively on YOLO11 projects. + +### How can I view the Weights & Biases dashboard for my YOLO11 training? + +After running your training script with W&B integration: + +1. A link to your W&B dashboard will be provided in the console output. +2. Click on the link or go to [wandb.ai](https://wandb.ai) and log in to your account. +3. Navigate to your project to view detailed metrics, visualizations, and model performance data. + +The dashboard offers insights into your model's training process, allowing you to analyze and improve your YOLO11 models effectively. + +### Can I disable Weights & Biases logging for YOLO11 training? + +Yes, you can disable W&B logging using the following command: + +```bash +wandb disabled +``` + +To re-enable logging, use: + +```bash +wandb enabled +``` + +This allows you to control when you want to use W&B logging without modifying your training scripts. + +### How does Weights & Biases help in optimizing YOLO11 models? + +Weights & Biases helps optimize YOLO11 models by: + +1. Providing detailed visualizations of training metrics +2. Enabling easy comparison between different model versions +3. Offering tools for [hyperparameter tuning](https://www.ultralytics.com/glossary/hyperparameter-tuning) +4. Allowing for collaborative analysis of model performance +5. Facilitating easy sharing of model artifacts and results + +These features help researchers and developers iterate faster and make data-driven decisions to improve their YOLO11 models. diff --git a/docs/mkdocs_github_authors.yaml b/docs/mkdocs_github_authors.yaml index 039caa38..0e0423c2 100644 --- a/docs/mkdocs_github_authors.yaml +++ b/docs/mkdocs_github_authors.yaml @@ -112,6 +112,9 @@ lakshantha@ultralytics.com: lakshanthad@yahoo.com: avatar: https://avatars.githubusercontent.com/u/20147381?v=4 username: lakshanthad +makei05@outlook.de: + avatar: https://avatars.githubusercontent.com/u/78843978?v=4 + username: Skillnoob matthewnoyce@icloud.com: avatar: https://avatars.githubusercontent.com/u/131261051?v=4 username: MatthewNoyce