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Keywords {🔍}

yolo, model, export, imgsz, cli, solutions, train, ultralytics, batch, device, predict, arguments, val, command, modelyolonpt, half, tasks, run, nms, int, models, configuration, detect, file, epochs, format, validate, official, onnx, count, default, formats, task, mode, args, custom, pairs, full, page, data, detection, quickstart, python, pose, overriding, line, interface, training, accuracy, trained,

Topics {✒️}

top previous quickstart ultralytics yolo models export yolo models ultralytics yolo cli yolo val model=yolo11n yolo models yolo predict model=yolo11n yolo train data=coco8 yolo copy-cfg command mastering ultralytics yolo customization table yolo export model=yolo11n ultralytics solutions ultralytics export formats run yolo solutions yaml configuration file benchmark] - args benchmark] args obb] - mode pre-built solutions ultralytics cli cli supports running command line interface solutions simplify implementation full configuration guide pt source='https tasks directly complex tasks python environment python code obb] segment classify track single-line command pose estimation trained yolo model arg=val pairs solutions command pt predict pt data=coco8 tasks current working directory val mode configuration guide val section solutions page full export details yolo cfg

Questions {❓}

• How can I validate the accuracy of a trained YOLO model using the CLI?
• How do I use the Ultralytics YOLO command line interface (CLI) for model training?
• How do I use the pre-built solutions in the Ultralytics CLI?
• What formats can I export my YOLO models to using the CLI?
• What tasks can I perform with the Ultralytics YOLO CLI?

Schema {🗺️}

["Article","FAQPage"]:
      context:https://schema.org
      headline:CLI
      image:
         https://img.youtube.com/vi/GsXGnb-A4Kc/maxresdefault.jpg
      datePublished:2023-11-12 02:49:37 +0100
      dateModified:2025-03-20 20:24:06 +0100
      author:
            type:Organization
            name:Ultralytics
            url:https://ultralytics.com/
      abstract:Explore the YOLO command line interface (CLI) for easy execution of detection tasks without needing a Python environment.
      mainEntity:
            type:Question
            name:How do I use the Ultralytics YOLO command line interface (CLI) for model training?
            acceptedAnswer:
               type:Answer
               text:To train a model using the CLI, execute a single-line command in the terminal. For example, to train a detection model for 10 epochs with a learning rate of 0.01, run: This command uses the train mode with specific arguments. For a full list of available arguments, refer to the Configuration Guide.
            type:Question
            name:What tasks can I perform with the Ultralytics YOLO CLI?
            acceptedAnswer:
               type:Answer
               text:The Ultralytics YOLO CLI supports various tasks, including detection, segmentation, classification, pose estimation, and oriented bounding box detection. You can also perform operations like: Customize each task with various arguments. For detailed syntax and examples, see the respective sections like Train, Predict, and Export.
            type:Question
            name:How can I validate the accuracy of a trained YOLO model using the CLI?
            acceptedAnswer:
               type:Answer
               text:To validate a model's accuracy, use the val mode. For example, to validate a pretrained detection model with a batch size of 1 and an image size of 640, run: This command evaluates the model on the specified dataset and provides performance metrics like mAP, precision, and recall. For more details, refer to the Val section.
            type:Question
            name:What formats can I export my YOLO models to using the CLI?
            acceptedAnswer:
               type:Answer
               text:You can export YOLO models to various formats including ONNX, TensorRT, CoreML, TensorFlow, and more. For instance, to export a model to ONNX format, run: The export command supports numerous options to optimize your model for specific deployment environments. For complete details on all available export formats and their specific parameters, visit the Export page.
            type:Question
            name:How do I use the pre-built solutions in the Ultralytics CLI?
            acceptedAnswer:
               type:Answer
               text:Ultralytics provides ready-to-use solutions through the solutions command. For example, to count objects in a video: These solutions require minimal configuration and provide immediate functionality for common computer vision tasks. To see all available solutions, run yolo solutions help. Each solution has specific parameters that can be customized to fit your needs.
Organization:
      name:Ultralytics
      url:https://ultralytics.com/
Question:
      name:How do I use the Ultralytics YOLO command line interface (CLI) for model training?
      acceptedAnswer:
         type:Answer
         text:To train a model using the CLI, execute a single-line command in the terminal. For example, to train a detection model for 10 epochs with a learning rate of 0.01, run: This command uses the train mode with specific arguments. For a full list of available arguments, refer to the Configuration Guide.
      name:What tasks can I perform with the Ultralytics YOLO CLI?
      acceptedAnswer:
         type:Answer
         text:The Ultralytics YOLO CLI supports various tasks, including detection, segmentation, classification, pose estimation, and oriented bounding box detection. You can also perform operations like: Customize each task with various arguments. For detailed syntax and examples, see the respective sections like Train, Predict, and Export.
      name:How can I validate the accuracy of a trained YOLO model using the CLI?
      acceptedAnswer:
         type:Answer
         text:To validate a model's accuracy, use the val mode. For example, to validate a pretrained detection model with a batch size of 1 and an image size of 640, run: This command evaluates the model on the specified dataset and provides performance metrics like mAP, precision, and recall. For more details, refer to the Val section.
      name:What formats can I export my YOLO models to using the CLI?
      acceptedAnswer:
         type:Answer
         text:You can export YOLO models to various formats including ONNX, TensorRT, CoreML, TensorFlow, and more. For instance, to export a model to ONNX format, run: The export command supports numerous options to optimize your model for specific deployment environments. For complete details on all available export formats and their specific parameters, visit the Export page.
      name:How do I use the pre-built solutions in the Ultralytics CLI?
      acceptedAnswer:
         type:Answer
         text:Ultralytics provides ready-to-use solutions through the solutions command. For example, to count objects in a video: These solutions require minimal configuration and provide immediate functionality for common computer vision tasks. To see all available solutions, run yolo solutions help. Each solution has specific parameters that can be customized to fit your needs.
Answer:
      text:To train a model using the CLI, execute a single-line command in the terminal. For example, to train a detection model for 10 epochs with a learning rate of 0.01, run: This command uses the train mode with specific arguments. For a full list of available arguments, refer to the Configuration Guide.
      text:The Ultralytics YOLO CLI supports various tasks, including detection, segmentation, classification, pose estimation, and oriented bounding box detection. You can also perform operations like: Customize each task with various arguments. For detailed syntax and examples, see the respective sections like Train, Predict, and Export.
      text:To validate a model's accuracy, use the val mode. For example, to validate a pretrained detection model with a batch size of 1 and an image size of 640, run: This command evaluates the model on the specified dataset and provides performance metrics like mAP, precision, and recall. For more details, refer to the Val section.
      text:You can export YOLO models to various formats including ONNX, TensorRT, CoreML, TensorFlow, and more. For instance, to export a model to ONNX format, run: The export command supports numerous options to optimize your model for specific deployment environments. For complete details on all available export formats and their specific parameters, visit the Export page.
      text:Ultralytics provides ready-to-use solutions through the solutions command. For example, to count objects in a video: These solutions require minimal configuration and provide immediate functionality for common computer vision tasks. To see all available solutions, run yolo solutions help. Each solution has specific parameters that can be customized to fit your needs.

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