DOCS . ULTRALYTICS . COM {
}
Title:
Model Export with Ultralytics YOLO - Ultralytics YOLO Docs
Description:
Learn how to export your YOLO11 model to various formats like ONNX, TensorRT, and CoreML. Achieve maximum compatibility and performance.
Website Age:
11 years and 4 months (reg. 2014-02-13).
Matching Content Categories {📚}
- Photography
- Technology & Computing
- Business & Finance
Content Management System {📝}
What CMS is docs.ultralytics.com built with?
Custom-built
No common CMS systems were detected on Docs.ultralytics.com, and no known web development framework was identified.
Traffic Estimate {📈}
What is the average monthly size of docs.ultralytics.com audience?
🌟 Strong Traffic: 100k - 200k visitors per month
Based on our best estimate, this website will receive around 127,142 visitors per month in the current month.
check SE Ranking
check Ahrefs
check Similarweb
check Ubersuggest
check Semrush
How Does Docs.ultralytics.com Make Money? {💸}
We see no obvious way the site makes money.
Earning money isn't the goal of every website; some are designed to offer support or promote social causes. People have different reasons for creating websites. This might be one such reason. Docs.ultralytics.com might be cashing in, but we can't detect the method they're using.
Keywords {🔍}
model, export, int, tensorrt, device, onnx, yolo, imgsz, format, batch, exporting, quantization, size, models, inference, formats, input, performance, half, ultralytics, dynamic, nms, exported, arguments, compatibility, devices, load, bool, mode, openvino, hardware, cli, python, torchscript, false, edge, data, dataset, fraction, predict, optimizing, coreml, gpu, speedup, enables, specific, exports, key, usage, enable,
Topics {✒️}
depth guides ultralytics yolo11 offers yolo predict model=yolo11n performs post-training quantization exporting yolo models real-time inference applications ultralytics top previous predict proper configuration ensures balancing memory usage desired image size yolo11 models exported configuring export arguments reducing model size optimizing processing efficiency key export arguments real-world applications specialized export formats comprehensive guide aims input shapes seamlessly dynamic input size potentially improving performance improving inference performance fit specific requirements run live inference quicker inference times edge ai deployments dynamic input sizing yolo11 export formats usage examples optimal quantization results 5x gpu speedup specific hardware setup device tf graphdef device tf lite dynamic input sizes achieve faster inference 3x cpu speedup minimal accuracy loss latest supported version dataset configuration file google coral devices edge tpu exports enables fp16 quantization predict mode optimizing model performance smooth exporting experience including advanced options applies specific optimizations model universally deployable
Questions {❓}
- How do I enable INT8 quantization when exporting my YOLO11 model?
- How do I export a YOLO11 model to ONNX format?
- What are the benefits of using TensorRT for model export?
- What are the key export arguments to consider for optimizing model performance?
- Why Choose YOLO11's Export Mode?
- Why is dynamic input size important when exporting models?
Schema {🗺️}
["Article","FAQPage"]:
context:https://schema.org
headline:Export
image:
https://github.com/ultralytics/docs/releases/download/0/ultralytics-yolov8-ecosystem-integrations.avif
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:Learn how to export your YOLO11 model to various formats like ONNX, TensorRT, and CoreML. Achieve maximum compatibility and performance.
mainEntity:
type:Question
name:How do I export a YOLO11 model to ONNX format?
acceptedAnswer:
type:Answer
text:Exporting a YOLO11 model to ONNX format is straightforward with Ultralytics. It provides both Python and CLI methods for exporting models. For more details on the process, including advanced options like handling different input sizes, refer to the ONNX integration guide.
type:Question
name:What are the benefits of using TensorRT for model export?
acceptedAnswer:
type:Answer
text:Using TensorRT for model export offers significant performance improvements. YOLO11 models exported to TensorRT can achieve up to a 5x GPU speedup, making it ideal for real-time inference applications. To learn more about integrating TensorRT, see the TensorRT integration guide.
type:Question
name:How do I enable INT8 quantization when exporting my YOLO11 model?
acceptedAnswer:
type:Answer
text:INT8 quantization is an excellent way to compress the model and speed up inference, especially on edge devices. Here's how you can enable INT8 quantization: INT8 quantization can be applied to various formats, such as TensorRT, OpenVINO, and CoreML. For optimal quantization results, provide a representative dataset using the data parameter.
type:Question
name:Why is dynamic input size important when exporting models?
acceptedAnswer:
type:Answer
text:Dynamic input size allows the exported model to handle varying image dimensions, providing flexibility and optimizing processing efficiency for different use cases. When exporting to formats like ONNX or TensorRT, enabling dynamic input size ensures that the model can adapt to different input shapes seamlessly. To enable this feature, use the dynamic=True flag during export: Dynamic input sizing is particularly useful for applications where input dimensions may vary, such as video processing or when handling images from different sources.
type:Question
name:What are the key export arguments to consider for optimizing model performance?
acceptedAnswer:
type:Answer
text:Understanding and configuring export arguments is crucial for optimizing model performance: For deployment on specific hardware platforms, consider using specialized export formats like TensorRT for NVIDIA GPUs, CoreML for Apple devices, or Edge TPU for Google Coral devices.
Organization:
name:Ultralytics
url:https://ultralytics.com/
Question:
name:How do I export a YOLO11 model to ONNX format?
acceptedAnswer:
type:Answer
text:Exporting a YOLO11 model to ONNX format is straightforward with Ultralytics. It provides both Python and CLI methods for exporting models. For more details on the process, including advanced options like handling different input sizes, refer to the ONNX integration guide.
name:What are the benefits of using TensorRT for model export?
acceptedAnswer:
type:Answer
text:Using TensorRT for model export offers significant performance improvements. YOLO11 models exported to TensorRT can achieve up to a 5x GPU speedup, making it ideal for real-time inference applications. To learn more about integrating TensorRT, see the TensorRT integration guide.
name:How do I enable INT8 quantization when exporting my YOLO11 model?
acceptedAnswer:
type:Answer
text:INT8 quantization is an excellent way to compress the model and speed up inference, especially on edge devices. Here's how you can enable INT8 quantization: INT8 quantization can be applied to various formats, such as TensorRT, OpenVINO, and CoreML. For optimal quantization results, provide a representative dataset using the data parameter.
name:Why is dynamic input size important when exporting models?
acceptedAnswer:
type:Answer
text:Dynamic input size allows the exported model to handle varying image dimensions, providing flexibility and optimizing processing efficiency for different use cases. When exporting to formats like ONNX or TensorRT, enabling dynamic input size ensures that the model can adapt to different input shapes seamlessly. To enable this feature, use the dynamic=True flag during export: Dynamic input sizing is particularly useful for applications where input dimensions may vary, such as video processing or when handling images from different sources.
name:What are the key export arguments to consider for optimizing model performance?
acceptedAnswer:
type:Answer
text:Understanding and configuring export arguments is crucial for optimizing model performance: For deployment on specific hardware platforms, consider using specialized export formats like TensorRT for NVIDIA GPUs, CoreML for Apple devices, or Edge TPU for Google Coral devices.
Answer:
text:Exporting a YOLO11 model to ONNX format is straightforward with Ultralytics. It provides both Python and CLI methods for exporting models. For more details on the process, including advanced options like handling different input sizes, refer to the ONNX integration guide.
text:Using TensorRT for model export offers significant performance improvements. YOLO11 models exported to TensorRT can achieve up to a 5x GPU speedup, making it ideal for real-time inference applications. To learn more about integrating TensorRT, see the TensorRT integration guide.
text:INT8 quantization is an excellent way to compress the model and speed up inference, especially on edge devices. Here's how you can enable INT8 quantization: INT8 quantization can be applied to various formats, such as TensorRT, OpenVINO, and CoreML. For optimal quantization results, provide a representative dataset using the data parameter.
text:Dynamic input size allows the exported model to handle varying image dimensions, providing flexibility and optimizing processing efficiency for different use cases. When exporting to formats like ONNX or TensorRT, enabling dynamic input size ensures that the model can adapt to different input shapes seamlessly. To enable this feature, use the dynamic=True flag during export: Dynamic input sizing is particularly useful for applications where input dimensions may vary, such as video processing or when handling images from different sources.
text:Understanding and configuring export arguments is crucial for optimizing model performance: For deployment on specific hardware platforms, consider using specialized export formats like TensorRT for NVIDIA GPUs, CoreML for Apple devices, or Edge TPU for Google Coral devices.
Social Networks {👍}(4)
External Links {🔗}(23)
- What are the total earnings of https://www.ultralytics.com/?
- How much profit does https://github.com/ultralytics/ultralytics generate?
- How much revenue does https://github.com/ultralytics/ultralytics/tree/main/docs/en/modes/export.md bring in?
- What's the financial intake of https://www.ultralytics.com/glossary/tensorflow?
- How much does https://www.ultralytics.com/glossary/accuracy pull in?
- What's the financial gain of https://www.ultralytics.com/blog/understanding-the-real-world-applications-of-edge-ai?
- What's the financial outcome of https://pytorch.org/?
- What is the monthly revenue of https://www.ultralytics.com/blog/deploying-computer-vision-applications-on-edge-ai-devices?
- How much does https://github.com/glenn-jocher pull in monthly?
- How much profit does https://github.com/Burhan-Q generate?
- Discover the revenue of https://github.com/UltralyticsAssistant
- Earnings of https://github.com/ambitious-octopus
- What's the financial intake of https://github.com/Kayzwer?
- How much income does https://github.com/Y-T-G have?
- What is the monthly revenue of https://github.com/jk4e?
- Explore the financials of https://github.com/MatthewNoyce
- Monthly income for https://github.com/RizwanMunawar
- Check the income stats for https://squidfunk.github.io/mkdocs-material/
- Check the income stats for https://github.com/ultralytics
- Revenue of https://x.com/ultralytics
- https://hub.docker.com/r/ultralytics/ultralytics/'s financial summary
- Revenue of https://pypi.org/project/ultralytics/
- What's the total monthly financial gain of https://discord.com/invite/ultralytics?
Analytics and Tracking {📊}
- Google Analytics
- Google Analytics 4
- Google Tag Manager
Libraries {📚}
- Clipboard.js
CDN Services {📦}
- Cloudflare
- Jsdelivr
- Weglot