Update Docs for 'ultralytics - ccd2cf0'

modes/train/index.html

@@ -23,7 +23,7 @@
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-<meta content="Train" name="title"/><link href="https://cdnjs.cloudflare.com/ajax/libs/font-awesome/6.4.2/css/all.min.css" rel="stylesheet"/><meta content="Ultralytics, YOLO11, model training, deep learning, object detection, GPU training, dataset augmentation, hyperparameter tuning, model performance, apple silicon training" name="keywords"/><meta content="website" property="og:type"/><meta content="https://docs.ultralytics.com/modes/train" property="og:url"/><meta content="Train" property="og:title"/><meta content="Learn how to efficiently train object detection models using YOLO11 with comprehensive instructions on settings, augmentation, and hardware utilization." property="og:description"/><meta content="https://github.com/ultralytics/docs/releases/download/0/ultralytics-yolov8-ecosystem-integrations.avif" property="og:image"/><meta content="summary_large_image" property="twitter:card"/><meta content="https://docs.ultralytics.com/modes/train" property="twitter:url"/><meta content="Train" property="twitter:title"/><meta content="Learn how to efficiently train object detection models using YOLO11 with comprehensive instructions on settings, augmentation, and hardware utilization." property="twitter:description"/><meta content="https://github.com/ultralytics/docs/releases/download/0/ultralytics-yolov8-ecosystem-integrations.avif" property="twitter:image"/><script type="application/ld+json">{"@context": "https://schema.org", "@type": ["Article", "FAQPage"], "headline": "Train", "image": ["https://github.com/ultralytics/docs/releases/download/0/ultralytics-yolov8-ecosystem-integrations.avif"], "datePublished": "2023-11-12 02:49:37 +0100", "dateModified": "2024-11-05 05:52:21 +0530", "author": [{"@type": "Organization", "name": "Ultralytics", "url": "https://ultralytics.com/"}], "abstract": "Learn how to efficiently train object detection models using YOLO11 with comprehensive instructions on settings, augmentation, and hardware utilization.", "mainEntity": [{"@type": "Question", "name": "How do I train an object detection model using Ultralytics YOLO11?", "acceptedAnswer": {"@type": "Answer", "text": "To train an object detection model using Ultralytics YOLO11, you can either use the Python API or the CLI. Below is an example for both: For more details, refer to the Train Settings section."}}, {"@type": "Question", "name": "What are the key features of Ultralytics YOLO11's Train mode?", "acceptedAnswer": {"@type": "Answer", "text": "The key features of Ultralytics YOLO11's Train mode include: These features make training efficient and customizable to your needs. For more details, see the Key Features of Train Mode section."}}, {"@type": "Question", "name": "How do I resume training from an interrupted session in Ultralytics YOLO11?", "acceptedAnswer": {"@type": "Answer", "text": "To resume training from an interrupted session, set the resume argument to True and specify the path to the last saved checkpoint. Check the section on Resuming Interrupted Trainings for more information."}}, {"@type": "Question", "name": "Can I train YOLO11 models on Apple silicon chips?", "acceptedAnswer": {"@type": "Answer", "text": "Yes, Ultralytics YOLO11 supports training on Apple silicon chips utilizing the Metal Performance Shaders (MPS) framework. Specify 'mps' as your training device. For more details, refer to the Apple Silicon MPS Training section."}}, {"@type": "Question", "name": "What are the common training settings, and how do I configure them?", "acceptedAnswer": {"@type": "Answer", "text": "Ultralytics YOLO11 allows you to configure a variety of training settings such as batch size, learning rate, epochs, and more through arguments. Here's a brief overview: For an in-depth guide on training settings, check the Train Settings section."}}]}</script></head>
+<meta content="Train" name="title"/><link href="https://cdnjs.cloudflare.com/ajax/libs/font-awesome/6.4.2/css/all.min.css" rel="stylesheet"/><meta content="Ultralytics, YOLO11, model training, deep learning, object detection, GPU training, dataset augmentation, hyperparameter tuning, model performance, apple silicon training" name="keywords"/><meta content="website" property="og:type"/><meta content="https://docs.ultralytics.com/modes/train" property="og:url"/><meta content="Train" property="og:title"/><meta content="Learn how to efficiently train object detection models using YOLO11 with comprehensive instructions on settings, augmentation, and hardware utilization." property="og:description"/><meta content="https://github.com/ultralytics/docs/releases/download/0/ultralytics-yolov8-ecosystem-integrations.avif" property="og:image"/><meta content="summary_large_image" property="twitter:card"/><meta content="https://docs.ultralytics.com/modes/train" property="twitter:url"/><meta content="Train" property="twitter:title"/><meta content="Learn how to efficiently train object detection models using YOLO11 with comprehensive instructions on settings, augmentation, and hardware utilization." property="twitter:description"/><meta content="https://github.com/ultralytics/docs/releases/download/0/ultralytics-yolov8-ecosystem-integrations.avif" property="twitter:image"/><script type="application/ld+json">{"@context": "https://schema.org", "@type": ["Article", "FAQPage"], "headline": "Train", "image": ["https://github.com/ultralytics/docs/releases/download/0/ultralytics-yolov8-ecosystem-integrations.avif"], "datePublished": "2023-11-12 02:49:37 +0100", "dateModified": "2025-01-06 21:16:48 +0800", "author": [{"@type": "Organization", "name": "Ultralytics", "url": "https://ultralytics.com/"}], "abstract": "Learn how to efficiently train object detection models using YOLO11 with comprehensive instructions on settings, augmentation, and hardware utilization.", "mainEntity": [{"@type": "Question", "name": "How do I train an object detection model using Ultralytics YOLO11?", "acceptedAnswer": {"@type": "Answer", "text": "To train an object detection model using Ultralytics YOLO11, you can either use the Python API or the CLI. Below is an example for both: For more details, refer to the Train Settings section."}}, {"@type": "Question", "name": "What are the key features of Ultralytics YOLO11's Train mode?", "acceptedAnswer": {"@type": "Answer", "text": "The key features of Ultralytics YOLO11's Train mode include: These features make training efficient and customizable to your needs. For more details, see the Key Features of Train Mode section."}}, {"@type": "Question", "name": "How do I resume training from an interrupted session in Ultralytics YOLO11?", "acceptedAnswer": {"@type": "Answer", "text": "To resume training from an interrupted session, set the resume argument to True and specify the path to the last saved checkpoint. Check the section on Resuming Interrupted Trainings for more information."}}, {"@type": "Question", "name": "Can I train YOLO11 models on Apple silicon chips?", "acceptedAnswer": {"@type": "Answer", "text": "Yes, Ultralytics YOLO11 supports training on Apple silicon chips utilizing the Metal Performance Shaders (MPS) framework. Specify 'mps' as your training device. For more details, refer to the Apple Silicon MPS Training section."}}, {"@type": "Question", "name": "What are the common training settings, and how do I configure them?", "acceptedAnswer": {"@type": "Answer", "text": "Ultralytics YOLO11 allows you to configure a variety of training settings such as batch size, learning rate, epochs, and more through arguments. Here's a brief overview: For an in-depth guide on training settings, check the Train Settings section."}}]}</script></head>
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@@ -839,6 +839,10 @@ <h3 id="key-features-of-train-mode">Key Features of Train Mode</h3>
 </div>
 <h2 id="usage-examples">Usage Examples</h2>
 <p>Train YOLO11n on the COCO8 dataset for 100 <a href="https://www.ultralytics.com/glossary/epoch">epochs</a> at image size 640. The training device can be specified using the <code>device</code> argument. If no argument is passed GPU <code>device=0</code> will be used if available, otherwise <code>device='cpu'</code> will be used. See Arguments section below for a full list of training arguments.</p>
+<div class="admonition warning">
+<p class="admonition-title">Windows Multi-Processing Error</p>
+<p>On Windows, you may receive a <code>RuntimeError</code> when launching the training as a script. Add a <code>if __name__ == "__main__":</code> block before your training code to resolve it.</p>
+</div>
 <div class="admonition example">
 <p class="admonition-title">Single-GPU and CPU Training Example</p>
 <p>Device is determined automatically. If a GPU is available then it will be used, otherwise training will start on CPU.</p>
@@ -1611,11 +1615,14 @@ <h3 id="what-are-the-common-training-settings-and-how-do-i-configure-them">What
 <span class="date-item" title="This page was first created on November 12, 2023">
 <span class="hover-item">📅</span> Created 1 year ago
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-<span class="date-item" title="This page was last updated on November 05, 2024">
-<span class="hover-item">✏️</span> Updated 2 months ago
+<span class="date-item" title="This page was last updated on January 06, 2025">
+<span class="hover-item">✏️</span> Updated 0 days ago
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+<img alt="Y-T-G" class="hover-item" loading="lazy" src="https://avatars.githubusercontent.com/u/32206511?v=4&amp;s=96"/>
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