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We are analyzing https://docs.ultralytics.com/models/yolov3/.

Title:
YOLOv3, and YOLOv3u - Ultralytics YOLO Docs
Description:
Discover YOLOv3 and its variants YOLOv3-Ultralytics and YOLOv3u. Learn about their features, implementations, and support for object detection tasks.
Website Age:
11 years and 4 months (reg. 2014-02-13).

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Custom-built

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🌟 Strong Traffic: 100k - 200k visitors per month

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Keywords {🔍}

yolov, model, detection, yolovu, object, models, tasks, ultralytics, yolo, inference, modes, supported, yolovultralytics, train, head, features, sizes, training, python, joseph, anchorfree, cli, segment, makes, variants, cite, research, updated, objectnessfree, split, accuracy, original, export, documentation, epochs, results, details, refer, architecture, realtime, overview, key, examples, citations, acknowledgements, accurate, redmon, objects, variant, support,

Topics {✒️}

export docs pages introducing features journal={arxiv preprint arxiv pre-defined anchor boxes objectness-free split head running yolo models ultralytics yolov3 repository object detection tasks pre-trained models modes include inference updated detection head original yolo papers object detection algorithm key features section detection head design effective object detection object detection workflows pre-trained weights updated model incorporates realtime detection transformer improving detection robustness modes yolov3 anchor-free head real-world scenarios yolov3 added features yolov3-ultralytics feature extractor network a-glance view models support operational modes multi-label predictions ultralytics supports ultralytics' adaptation neural architecture search inference mode documentation yolo-world real-time yolov3 models supported tasks tasks supported yolov8 models simple yolov3 training yolo-nas train mode documentation comprehensive training options object detection modes �� yolov3u-spp ultralytics pt models

Questions {❓}

How can I train a YOLOv3 model using Ultralytics?
How can I use YOLOv3 models for inference?
What are the differences between YOLOv3, YOLOv3-Ultralytics, and YOLOv3u?
What makes YOLOv3u more accurate for object detection tasks?
What tasks are supported by YOLOv3 and its variants?
Where can I find resources to cite YOLOv3 in my research?

Schema {🗺️}

["Article","FAQPage"]:
      context:https://schema.org
      headline:YOLOv3
      image:
         https://github.com/ultralytics/docs/releases/download/0/ultralytics-yolov3-banner.avif
      datePublished:2023-11-12 02:49:37 +0100
      dateModified:2025-03-17 21:52:48 +0100
      author:
            type:Organization
            name:Ultralytics
            url:https://ultralytics.com/
      abstract:Discover YOLOv3 and its variants YOLOv3-Ultralytics and YOLOv3u. Learn about their features, implementations, and support for object detection tasks.
      mainEntity:
            type:Question
            name:What are the differences between YOLOv3, YOLOv3-Ultralytics, and YOLOv3u?
            acceptedAnswer:
               type:Answer
               text:YOLOv3 is the third iteration of the YOLO (You Only Look Once) object detection algorithm developed by Joseph Redmon, known for its balance of accuracy and speed, utilizing three different scales (13x13, 26x26, and 52x52) for detections. YOLOv3-Ultralytics is Ultralytics' adaptation of YOLOv3 that adds support for more pre-trained models and facilitates easier model customization. YOLOv3u is an upgraded variant of YOLOv3-Ultralytics, integrating the anchor-free, objectness-free split head from YOLOv8, improving detection robustness and accuracy for various object sizes. For more details on the variants, refer to the YOLOv3 series.
            type:Question
            name:How can I train a YOLOv3 model using Ultralytics?
            acceptedAnswer:
               type:Answer
               text:Training a YOLOv3 model with Ultralytics is straightforward. You can train the model using either Python or CLI: For more comprehensive training options and guidelines, visit our Train mode documentation.
            type:Question
            name:What makes YOLOv3u more accurate for object detection tasks?
            acceptedAnswer:
               type:Answer
               text:YOLOv3u improves upon YOLOv3 and YOLOv3-Ultralytics by incorporating the anchor-free, objectness-free split head used in YOLOv8 models. This upgrade eliminates the need for pre-defined anchor boxes and objectness scores, enhancing its capability to detect objects of varying sizes and shapes more precisely. This makes YOLOv3u a better choice for complex and diverse object detection tasks. For more information, refer to the Key Features section.
            type:Question
            name:How can I use YOLOv3 models for inference?
            acceptedAnswer:
               type:Answer
               text:You can perform inference using YOLOv3 models by either Python scripts or CLI commands: Refer to the Inference mode documentation for more details on running YOLO models.
            type:Question
            name:What tasks are supported by YOLOv3 and its variants?
            acceptedAnswer:
               type:Answer
               text:YOLOv3, YOLOv3-Tiny and YOLOv3-SPP primarily support object detection tasks. These models can be used for various stages of model deployment and development, such as Inference, Validation, Training, and Export. For a comprehensive set of tasks supported and more in-depth details, visit our Object Detection tasks documentation.
            type:Question
            name:Where can I find resources to cite YOLOv3 in my research?
            acceptedAnswer:
               type:Answer
               text:If you use YOLOv3 in your research, please cite the original YOLO papers and the Ultralytics YOLOv3 repository. Example BibTeX citation: For more citation details, refer to the Citations and Acknowledgements section.
Organization:
      name:Ultralytics
      url:https://ultralytics.com/
Question:
      name:What are the differences between YOLOv3, YOLOv3-Ultralytics, and YOLOv3u?
      acceptedAnswer:
         type:Answer
         text:YOLOv3 is the third iteration of the YOLO (You Only Look Once) object detection algorithm developed by Joseph Redmon, known for its balance of accuracy and speed, utilizing three different scales (13x13, 26x26, and 52x52) for detections. YOLOv3-Ultralytics is Ultralytics' adaptation of YOLOv3 that adds support for more pre-trained models and facilitates easier model customization. YOLOv3u is an upgraded variant of YOLOv3-Ultralytics, integrating the anchor-free, objectness-free split head from YOLOv8, improving detection robustness and accuracy for various object sizes. For more details on the variants, refer to the YOLOv3 series.
      name:How can I train a YOLOv3 model using Ultralytics?
      acceptedAnswer:
         type:Answer
         text:Training a YOLOv3 model with Ultralytics is straightforward. You can train the model using either Python or CLI: For more comprehensive training options and guidelines, visit our Train mode documentation.
      name:What makes YOLOv3u more accurate for object detection tasks?
      acceptedAnswer:
         type:Answer
         text:YOLOv3u improves upon YOLOv3 and YOLOv3-Ultralytics by incorporating the anchor-free, objectness-free split head used in YOLOv8 models. This upgrade eliminates the need for pre-defined anchor boxes and objectness scores, enhancing its capability to detect objects of varying sizes and shapes more precisely. This makes YOLOv3u a better choice for complex and diverse object detection tasks. For more information, refer to the Key Features section.
      name:How can I use YOLOv3 models for inference?
      acceptedAnswer:
         type:Answer
         text:You can perform inference using YOLOv3 models by either Python scripts or CLI commands: Refer to the Inference mode documentation for more details on running YOLO models.
      name:What tasks are supported by YOLOv3 and its variants?
      acceptedAnswer:
         type:Answer
         text:YOLOv3, YOLOv3-Tiny and YOLOv3-SPP primarily support object detection tasks. These models can be used for various stages of model deployment and development, such as Inference, Validation, Training, and Export. For a comprehensive set of tasks supported and more in-depth details, visit our Object Detection tasks documentation.
      name:Where can I find resources to cite YOLOv3 in my research?
      acceptedAnswer:
         type:Answer
         text:If you use YOLOv3 in your research, please cite the original YOLO papers and the Ultralytics YOLOv3 repository. Example BibTeX citation: For more citation details, refer to the Citations and Acknowledgements section.
Answer:
      text:YOLOv3 is the third iteration of the YOLO (You Only Look Once) object detection algorithm developed by Joseph Redmon, known for its balance of accuracy and speed, utilizing three different scales (13x13, 26x26, and 52x52) for detections. YOLOv3-Ultralytics is Ultralytics' adaptation of YOLOv3 that adds support for more pre-trained models and facilitates easier model customization. YOLOv3u is an upgraded variant of YOLOv3-Ultralytics, integrating the anchor-free, objectness-free split head from YOLOv8, improving detection robustness and accuracy for various object sizes. For more details on the variants, refer to the YOLOv3 series.
      text:Training a YOLOv3 model with Ultralytics is straightforward. You can train the model using either Python or CLI: For more comprehensive training options and guidelines, visit our Train mode documentation.
      text:YOLOv3u improves upon YOLOv3 and YOLOv3-Ultralytics by incorporating the anchor-free, objectness-free split head used in YOLOv8 models. This upgrade eliminates the need for pre-defined anchor boxes and objectness scores, enhancing its capability to detect objects of varying sizes and shapes more precisely. This makes YOLOv3u a better choice for complex and diverse object detection tasks. For more information, refer to the Key Features section.
      text:You can perform inference using YOLOv3 models by either Python scripts or CLI commands: Refer to the Inference mode documentation for more details on running YOLO models.
      text:YOLOv3, YOLOv3-Tiny and YOLOv3-SPP primarily support object detection tasks. These models can be used for various stages of model deployment and development, such as Inference, Validation, Training, and Export. For a comprehensive set of tasks supported and more in-depth details, visit our Object Detection tasks documentation.
      text:If you use YOLOv3 in your research, please cite the original YOLO papers and the Ultralytics YOLOv3 repository. Example BibTeX citation: For more citation details, refer to the Citations and Acknowledgements section.

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