DOCS . ULTRALYTICS . COM {
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Title:
Model Training with Ultralytics YOLO - Ultralytics YOLO Docs
Description:
Learn how to efficiently train object detection models using YOLO11 with comprehensive instructions on settings, augmentation, and hardware utilization.
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Keywords {🔍}
training, model, train, yolo, float, ultralytics, models, epochs, learning, device, batch, image, load, settings, performance, gpus, python, rate, bool, mode, gpu, mps, resume, dataset, cli, data, silicon, size, argument, results, imgsz, int, apple, false, fraction, features, multigpu, idle, tensorboard, automatically, import, weights, enables, true, object, multiple, pretrained, number, loss, str,
Topics {✒️}
resuming interrupted trainings reference section introducing label noise m1/m2/m3/m4 enable multi-gpu training multi-class datasets introducing color variability binary classification tasks windows multi-processing error ultralytics yolo models image processing tasks ultralytics yolo loads idle gpu training gpu memory usage modes choose ultralytics yolo enables multi-scale training multi-gpu systems multi-gpu setups apple silicon chips optimizing resource usage multi-gpu training introducing variability apple silicon chip prediction examples training tasks increased memory usage automatic selection based multi-gpu support fine-tune model performance time-constrained training scenarios optimal augmentation strategy manual gpu selection classification tasks yolo models encompass predefined augmentation policy multiple idle gpus train settings section yolo train data=coco training augmentation operations standard datasets reducing memory usage idle gpu deep learning models train mode include pose estimation models common training settings ultralytics yolo batch-size settings table outlines
Questions {❓}
- Can I train YOLO11 models on Apple silicon chips?
- How do I resume training from an interrupted session in Ultralytics YOLO11?
- How do I train an object detection model using Ultralytics YOLO11?
- What are the common training settings, and how do I configure them?
- What are the key features of Ultralytics YOLO11's Train mode?
- Why Choose Ultralytics YOLO for Training?
Schema {🗺️}
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context:https://schema.org
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:2025-06-22 19:21:22 +0100
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.
Organization:
name:Ultralytics
url:https://ultralytics.com/
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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