DOCS . ULTRALYTICS . COM {}

Analyzed Page
Matching Content Categories
CMS
Monthly Traffic Estimate
How Does Docs.ultralytics.com Make Money
Keywords
Topics
Questions
Schema
Social Networks
External Links
Analytics And Tracking
Libraries
CDN Services

We are analyzing https://docs.ultralytics.com/tasks/classify/.

Title:
Image Classification - Ultralytics YOLO Docs
Description:
Master image classification using YOLO11. Learn to train, validate, predict, and export models efficiently.
Website Age:
11 years and 4 months (reg. 2014-02-13).

Matching Content Categories {📚}

Photography
Style & Fashion
Crafts

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 100,019 visitors per month in the current month.
However, some sources were not loaded, we suggest to reload the page to get complete results.

check SE Ranking
check Ahrefs
check Similarweb
check Ubersuggest
check Semrush

How Does Docs.ultralytics.com Make Money? {💸}

We find it hard to spot revenue streams.

Websites don't always need to be profitable; some serve as platforms for education or personal expression. Websites can serve multiple purposes. And this might be one of them. Docs.ultralytics.com might be plotting its profit, but the way they're doing it isn't detectable yet.

Keywords {🔍}

model, yolo, classification, image, dataset, imgsz, models, export, load, device, batch, ultralytics, train, import, training, trained, class, pretrained, python, cli, predict, format, half, validate, yoloyolonclspt, custom, nms, int, classify, val, top, data, onnx, yoloncls, validation, arguments, transforms, customized, str, accuracy, tasks, imagenet, epochs, page, formats, yolonclspt, mnist, full, build, results,

Topics {✒️}

yolo predict model=yolo11n-cls ultralytics import yolo image classification tasks classify models latest ultralytics release yolo classification models top previous segment trained yolo11n-cls model pose models models download automatically tasks yolo11-cls export formats extreme aspect ratios trained yolo11 model enhanced dataset handling speed t4 tensorrt10 imagenet val images potentially causing loss speed cpu onnx dataset import classificationdataset customized classification dataset full export details classification standalone validation single class label consistent validation results detailed export options yolo11 models device tf savedmodel device tf graphdef device tf lite efficient image classification customized dataset class settings remembered metrics critical visual information ultralytics //ultralytics custom validation transforms customized trainer class customized validator class yolo11n-cls model detect table model = yolo exported models models section custom training transforms cropping transforms predict page trained model' /imagenet batch=1 device=0

Questions {❓}

How can I export a trained YOLO11 model to different formats?
How do I train a YOLO11 model for image classification?
How do I validate a trained YOLO11 classification model?
What is the purpose of YOLO11 in image classification?
Where can I find pretrained YOLO11 classification models?

Schema {🗺️}

["Article","FAQPage"]:
      context:https://schema.org
      headline:Classify
      image:
         https://github.com/ultralytics/docs/releases/download/0/image-classification-examples.avif
      datePublished:2023-11-12 02:49:37 +0100
      dateModified:2025-06-26 10:18:53 +0200
      author:
            type:Organization
            name:Ultralytics
            url:https://ultralytics.com/
      abstract:Master image classification using YOLO11. Learn to train, validate, predict, and export models efficiently.
      mainEntity:
            type:Question
            name:What is the purpose of YOLO11 in image classification?
            acceptedAnswer:
               type:Answer
               text:YOLO11 models, such as yolo11n-cls.pt, are designed for efficient image classification. They assign a single class label to an entire image along with a confidence score. This is particularly useful for applications where knowing the specific class of an image is sufficient, rather than identifying the location or shape of objects within the image.
            type:Question
            name:How do I train a YOLO11 model for image classification?
            acceptedAnswer:
               type:Answer
               text:To train a YOLO11 model, you can use either Python or CLI commands. For example, to train a yolo11n-cls model on the MNIST160 dataset for 100 epochs at an image size of 64: For more configuration options, visit the Configuration page.
            type:Question
            name:Where can I find pretrained YOLO11 classification models?
            acceptedAnswer:
               type:Answer
               text:Pretrained YOLO11 classification models can be found in the Models section. Models like yolo11n-cls.pt, yolo11s-cls.pt, yolo11m-cls.pt, etc., are pretrained on the ImageNet dataset and can be easily downloaded and used for various image classification tasks.
            type:Question
            name:How can I export a trained YOLO11 model to different formats?
            acceptedAnswer:
               type:Answer
               text:You can export a trained YOLO11 model to various formats using Python or CLI commands. For instance, to export a model to ONNX format: For detailed export options, refer to the Export page.
            type:Question
            name:How do I validate a trained YOLO11 classification model?
            acceptedAnswer:
               type:Answer
               text:To validate a trained model's accuracy on a dataset like MNIST160, you can use the following Python or CLI commands: For more information, visit the Validate section.
Organization:
      name:Ultralytics
      url:https://ultralytics.com/
Question:
      name:What is the purpose of YOLO11 in image classification?
      acceptedAnswer:
         type:Answer
         text:YOLO11 models, such as yolo11n-cls.pt, are designed for efficient image classification. They assign a single class label to an entire image along with a confidence score. This is particularly useful for applications where knowing the specific class of an image is sufficient, rather than identifying the location or shape of objects within the image.
      name:How do I train a YOLO11 model for image classification?
      acceptedAnswer:
         type:Answer
         text:To train a YOLO11 model, you can use either Python or CLI commands. For example, to train a yolo11n-cls model on the MNIST160 dataset for 100 epochs at an image size of 64: For more configuration options, visit the Configuration page.
      name:Where can I find pretrained YOLO11 classification models?
      acceptedAnswer:
         type:Answer
         text:Pretrained YOLO11 classification models can be found in the Models section. Models like yolo11n-cls.pt, yolo11s-cls.pt, yolo11m-cls.pt, etc., are pretrained on the ImageNet dataset and can be easily downloaded and used for various image classification tasks.
      name:How can I export a trained YOLO11 model to different formats?
      acceptedAnswer:
         type:Answer
         text:You can export a trained YOLO11 model to various formats using Python or CLI commands. For instance, to export a model to ONNX format: For detailed export options, refer to the Export page.
      name:How do I validate a trained YOLO11 classification model?
      acceptedAnswer:
         type:Answer
         text:To validate a trained model's accuracy on a dataset like MNIST160, you can use the following Python or CLI commands: For more information, visit the Validate section.
Answer:
      text:YOLO11 models, such as yolo11n-cls.pt, are designed for efficient image classification. They assign a single class label to an entire image along with a confidence score. This is particularly useful for applications where knowing the specific class of an image is sufficient, rather than identifying the location or shape of objects within the image.
      text:To train a YOLO11 model, you can use either Python or CLI commands. For example, to train a yolo11n-cls model on the MNIST160 dataset for 100 epochs at an image size of 64: For more configuration options, visit the Configuration page.
      text:Pretrained YOLO11 classification models can be found in the Models section. Models like yolo11n-cls.pt, yolo11s-cls.pt, yolo11m-cls.pt, etc., are pretrained on the ImageNet dataset and can be easily downloaded and used for various image classification tasks.
      text:You can export a trained YOLO11 model to various formats using Python or CLI commands. For instance, to export a model to ONNX format: For detailed export options, refer to the Export page.
      text:To validate a trained model's accuracy on a dataset like MNIST160, you can use the following Python or CLI commands: For more information, visit the Validate section.

Social Networks {👍}(4)

External Links {🔗}(37)

Analytics and Tracking {📊}

Google Analytics
Google Analytics 4
Google Tag Manager

Libraries {📚}

Clipboard.js

CDN Services {📦}

Cloudflare
Jsdelivr
Weglot

3.2s.