DOCS . ULTRALYTICS . COM {
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Title:
CoreML Export for YOLO11 Models - Ultralytics YOLO Docs
Description:
Learn how to export YOLO11 models to CoreML for optimized, on-device machine learning on iOS and macOS. Follow step-by-step instructions.
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Keywords {🔍}
coreml, models, yolo, model, ultralytics, export, format, deployment, performance, inference, app, exported, devices, guide, exporting, deploying, ondevice, usage, ios, options, machine, cli, installation, directly, learning, device, integration, features, run, processing, core, optimization, updates, size, install, python, tasks, neural, ensure, optimized, apple, seamless, macos, apples, framework, supports, api, offers, privacy, cpu,
Topics {✒️}
ultralytics yolo11 integrations supports domain-specific tasks machine learning tasks solutions top previous tensorrt ultralytics yolo11 models export arguments argument supports tasks efficient detection post-processing ultralytics documentation page ultralytics package installed needing regular updates deploying coreml models edge devices deploying yolo11 models machine-learning model format coreml enables optimized yolo11 models exported machine learning models core ml model host coreml models exported coreml model interactive ml experiences yolo11 models offered export yolo11 models executing models directly exporting yolo11 models yolo11 installation guide coreml deployment options reducing model size efficient object detection code implementing coreml models require frequent updates run inference directly x86 linux machine desired image size coreml official documentation multiple deployment options adjusts inference based cloud-based deployment means model inference coreml export guide coreml export format metal performance shaders coreml format model device machine learning ensures data privacy ensures seamless performance ensuring low latency
Questions {❓}
- Can I run inference directly with the exported CoreML model?
- How do I export YOLO11 models to CoreML format?
- How does CoreML ensure optimized performance for YOLO11 models?
- What are the benefits of using CoreML for deploying YOLO11 models?
- What are the deployment options for YOLO11 models exported to CoreML?
Schema {🗺️}
["Article","FAQPage"]:
context:https://schema.org
headline:CoreML
image:
https://github.com/ultralytics/docs/releases/download/0/coreml-overview.avif
datePublished:2024-02-07 04:14:51 +0300
dateModified:2025-06-26 12:13:28 +0600
author:
type:Organization
name:Ultralytics
url:https://ultralytics.com/
abstract:Learn how to export YOLO11 models to CoreML for optimized, on-device machine learning on iOS and macOS. Follow step-by-step instructions.
mainEntity:
type:Question
name:How do I export YOLO11 models to CoreML format?
acceptedAnswer:
type:Answer
text:To export your Ultralytics YOLO11 models to CoreML format, you'll first need to ensure you have the ultralytics package installed. You can install it using: Next, you can export the model using the following Python or CLI commands: For further details, refer to the Exporting YOLO11 Models to CoreML section of our documentation.
type:Question
name:What are the benefits of using CoreML for deploying YOLO11 models?
acceptedAnswer:
type:Answer
text:CoreML provides numerous advantages for deploying Ultralytics YOLO11 models on Apple devices: For more details on integrating your CoreML model into an iOS app, check out the guide on Integrating a Core ML Model into Your App.
type:Question
name:What are the deployment options for YOLO11 models exported to CoreML?
acceptedAnswer:
type:Answer
text:Once you export your YOLO11 model to CoreML format, you have multiple deployment options: For detailed guidance on deploying CoreML models, refer to CoreML Deployment Options.
type:Question
name:How does CoreML ensure optimized performance for YOLO11 models?
acceptedAnswer:
type:Answer
text:CoreML ensures optimized performance for Ultralytics YOLO11 models by utilizing various optimization techniques: For more information on performance optimization, visit the CoreML official documentation.
type:Question
name:Can I run inference directly with the exported CoreML model?
acceptedAnswer:
type:Answer
text:Yes, you can run inference directly using the exported CoreML model. Below are the commands for Python and CLI: For additional information, refer to the Usage section of the CoreML export guide.
Organization:
name:Ultralytics
url:https://ultralytics.com/
Question:
name:How do I export YOLO11 models to CoreML format?
acceptedAnswer:
type:Answer
text:To export your Ultralytics YOLO11 models to CoreML format, you'll first need to ensure you have the ultralytics package installed. You can install it using: Next, you can export the model using the following Python or CLI commands: For further details, refer to the Exporting YOLO11 Models to CoreML section of our documentation.
name:What are the benefits of using CoreML for deploying YOLO11 models?
acceptedAnswer:
type:Answer
text:CoreML provides numerous advantages for deploying Ultralytics YOLO11 models on Apple devices: For more details on integrating your CoreML model into an iOS app, check out the guide on Integrating a Core ML Model into Your App.
name:What are the deployment options for YOLO11 models exported to CoreML?
acceptedAnswer:
type:Answer
text:Once you export your YOLO11 model to CoreML format, you have multiple deployment options: For detailed guidance on deploying CoreML models, refer to CoreML Deployment Options.
name:How does CoreML ensure optimized performance for YOLO11 models?
acceptedAnswer:
type:Answer
text:CoreML ensures optimized performance for Ultralytics YOLO11 models by utilizing various optimization techniques: For more information on performance optimization, visit the CoreML official documentation.
name:Can I run inference directly with the exported CoreML model?
acceptedAnswer:
type:Answer
text:Yes, you can run inference directly using the exported CoreML model. Below are the commands for Python and CLI: For additional information, refer to the Usage section of the CoreML export guide.
Answer:
text:To export your Ultralytics YOLO11 models to CoreML format, you'll first need to ensure you have the ultralytics package installed. You can install it using: Next, you can export the model using the following Python or CLI commands: For further details, refer to the Exporting YOLO11 Models to CoreML section of our documentation.
text:CoreML provides numerous advantages for deploying Ultralytics YOLO11 models on Apple devices: For more details on integrating your CoreML model into an iOS app, check out the guide on Integrating a Core ML Model into Your App.
text:Once you export your YOLO11 model to CoreML format, you have multiple deployment options: For detailed guidance on deploying CoreML models, refer to CoreML Deployment Options.
text:CoreML ensures optimized performance for Ultralytics YOLO11 models by utilizing various optimization techniques: For more information on performance optimization, visit the CoreML official documentation.
text:Yes, you can run inference directly using the exported CoreML model. Below are the commands for Python and CLI: For additional information, refer to the Usage section of the CoreML export guide.
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