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/datasets/classify/imagenet/.

Title:
ImageNet Dataset - Ultralytics YOLO Docs
Description:
Explore the extensive ImageNet dataset and discover its role in advancing deep learning in computer vision. Access pretrained models and training examples.
Website Age:
11 years and 4 months (reg. 2014-02-13).

Matching Content Categories {📚}

Photography
Careers
Virtual Reality

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 can't tell how the site generates income.

While many websites aim to make money, others are created to share knowledge or showcase creativity. People build websites for various reasons. This could be one of them. Docs.ultralytics.com might be earning cash quietly, but we haven't detected the monetization method.

Keywords {🔍}

imagenet, dataset, models, computer, vision, model, training, pretrained, image, yolo, visual, recognition, images, object, classification, challenge, learning, ultralytics, large, scale, ilsvrc, deep, tasks, detection, coco, research, wordnet, top, hierarchy, train, structure, annotations, largescale, category, development, load, datasets, objects, applications, important, role, annotated, million, synsets, speed, highresolution, organized, synset, annual, terms,

Topics {✒️}

top previous fashion-mnist image segmentation object detection tasks large-scale database consisting large-scale database object detection large-scale dataset including image classification pretrained yolo model imagenet dataset projects imagenet dataset structured large-scale pretrained models reduce deep learning models image classification dataset structure section computer vision tasks deep learning model standardized evaluation metrics advancing computer vision imagenet dataset li fei-fei} journal={international journal li fei-fei computer vision technologies real-time applications imagenet team imagenet website computational resources required imagenet make yolo11 models speed cpu onnx speed t4 tensorrt10 depth training instruction annotated images designed sheer volume provide accelerate development cycles model training page dataset structure pretrained model yolo11n-cls model 14 million images deep learning challenge training models imagenet sample images computer vision year={2015} developing robust

Questions {❓}

How can I use a pretrained YOLO model for image classification on the ImageNet dataset?
How is the ImageNet dataset structured, and why is it important?
What is the ImageNet dataset and how is it used in computer vision?
What role does the ImageNet Large Scale Visual Recognition Challenge (ILSVRC) play in computer vision?
Why should I use the Ultralytics YOLO11 pretrained models for my ImageNet dataset projects?

Schema {🗺️}

["Article","FAQPage"]:
      context:https://schema.org
      headline:ImageNet
      image:
         https://github.com/ultralytics/docs/releases/download/0/imagenet-sample-images.avif
      datePublished:2023-11-12 02:49:37 +0100
      dateModified:2025-03-17 21:52:48 +0100
      author:
            type:Organization
            name:Ultralytics
            url:https://ultralytics.com/
      abstract:Explore the extensive ImageNet dataset and discover its role in advancing deep learning in computer vision. Access pretrained models and training examples.
      mainEntity:
            type:Question
            name:What is the ImageNet dataset and how is it used in computer vision?
            acceptedAnswer:
               type:Answer
               text:The ImageNet dataset is a large-scale database consisting of over 14 million high-resolution images categorized using WordNet synsets. It is extensively used in visual object recognition research, including image classification and object detection. The dataset's annotations and sheer volume provide a rich resource for training deep learning models. Notably, models like AlexNet, VGG, and ResNet have been trained and benchmarked using ImageNet, showcasing its role in advancing computer vision.
            type:Question
            name:How can I use a pretrained YOLO model for image classification on the ImageNet dataset?
            acceptedAnswer:
               type:Answer
               text:To use a pretrained Ultralytics YOLO model for image classification on the ImageNet dataset, follow these steps: For more in-depth training instruction, refer to our Training page.
            type:Question
            name:Why should I use the Ultralytics YOLO11 pretrained models for my ImageNet dataset projects?
            acceptedAnswer:
               type:Answer
               text:Ultralytics YOLO11 pretrained models offer state-of-the-art performance in terms of speed and accuracy for various computer vision tasks. For example, the YOLO11n-cls model, with a top-1 accuracy of 70.0% and a top-5 accuracy of 89.4%, is optimized for real-time applications. Pretrained models reduce the computational resources required for training from scratch and accelerate development cycles. Learn more about the performance metrics of YOLO11 models in the ImageNet Pretrained Models section.
            type:Question
            name:How is the ImageNet dataset structured, and why is it important?
            acceptedAnswer:
               type:Answer
               text:The ImageNet dataset is organized using the WordNet hierarchy, where each node in the hierarchy represents a category described by a synset (a collection of synonymous terms). This structure allows for detailed annotations, making it ideal for training models to recognize a wide variety of objects. The diversity and annotation richness of ImageNet make it a valuable dataset for developing robust and generalizable deep learning models. More about this organization can be found in the Dataset Structure section.
            type:Question
            name:What role does the ImageNet Large Scale Visual Recognition Challenge (ILSVRC) play in computer vision?
            acceptedAnswer:
               type:Answer
               text:The annual ImageNet Large Scale Visual Recognition Challenge (ILSVRC) has been pivotal in driving advancements in computer vision by providing a competitive platform for evaluating algorithms on a large-scale, standardized dataset. It offers standardized evaluation metrics, fostering innovation and development in areas such as image classification, object detection, and image segmentation. The challenge has continuously pushed the boundaries of what is possible with deep learning and computer vision technologies.
Organization:
      name:Ultralytics
      url:https://ultralytics.com/
Question:
      name:What is the ImageNet dataset and how is it used in computer vision?
      acceptedAnswer:
         type:Answer
         text:The ImageNet dataset is a large-scale database consisting of over 14 million high-resolution images categorized using WordNet synsets. It is extensively used in visual object recognition research, including image classification and object detection. The dataset's annotations and sheer volume provide a rich resource for training deep learning models. Notably, models like AlexNet, VGG, and ResNet have been trained and benchmarked using ImageNet, showcasing its role in advancing computer vision.
      name:How can I use a pretrained YOLO model for image classification on the ImageNet dataset?
      acceptedAnswer:
         type:Answer
         text:To use a pretrained Ultralytics YOLO model for image classification on the ImageNet dataset, follow these steps: For more in-depth training instruction, refer to our Training page.
      name:Why should I use the Ultralytics YOLO11 pretrained models for my ImageNet dataset projects?
      acceptedAnswer:
         type:Answer
         text:Ultralytics YOLO11 pretrained models offer state-of-the-art performance in terms of speed and accuracy for various computer vision tasks. For example, the YOLO11n-cls model, with a top-1 accuracy of 70.0% and a top-5 accuracy of 89.4%, is optimized for real-time applications. Pretrained models reduce the computational resources required for training from scratch and accelerate development cycles. Learn more about the performance metrics of YOLO11 models in the ImageNet Pretrained Models section.
      name:How is the ImageNet dataset structured, and why is it important?
      acceptedAnswer:
         type:Answer
         text:The ImageNet dataset is organized using the WordNet hierarchy, where each node in the hierarchy represents a category described by a synset (a collection of synonymous terms). This structure allows for detailed annotations, making it ideal for training models to recognize a wide variety of objects. The diversity and annotation richness of ImageNet make it a valuable dataset for developing robust and generalizable deep learning models. More about this organization can be found in the Dataset Structure section.
      name:What role does the ImageNet Large Scale Visual Recognition Challenge (ILSVRC) play in computer vision?
      acceptedAnswer:
         type:Answer
         text:The annual ImageNet Large Scale Visual Recognition Challenge (ILSVRC) has been pivotal in driving advancements in computer vision by providing a competitive platform for evaluating algorithms on a large-scale, standardized dataset. It offers standardized evaluation metrics, fostering innovation and development in areas such as image classification, object detection, and image segmentation. The challenge has continuously pushed the boundaries of what is possible with deep learning and computer vision technologies.
Answer:
      text:The ImageNet dataset is a large-scale database consisting of over 14 million high-resolution images categorized using WordNet synsets. It is extensively used in visual object recognition research, including image classification and object detection. The dataset's annotations and sheer volume provide a rich resource for training deep learning models. Notably, models like AlexNet, VGG, and ResNet have been trained and benchmarked using ImageNet, showcasing its role in advancing computer vision.
      text:To use a pretrained Ultralytics YOLO model for image classification on the ImageNet dataset, follow these steps: For more in-depth training instruction, refer to our Training page.
      text:Ultralytics YOLO11 pretrained models offer state-of-the-art performance in terms of speed and accuracy for various computer vision tasks. For example, the YOLO11n-cls model, with a top-1 accuracy of 70.0% and a top-5 accuracy of 89.4%, is optimized for real-time applications. Pretrained models reduce the computational resources required for training from scratch and accelerate development cycles. Learn more about the performance metrics of YOLO11 models in the ImageNet Pretrained Models section.
      text:The ImageNet dataset is organized using the WordNet hierarchy, where each node in the hierarchy represents a category described by a synset (a collection of synonymous terms). This structure allows for detailed annotations, making it ideal for training models to recognize a wide variety of objects. The diversity and annotation richness of ImageNet make it a valuable dataset for developing robust and generalizable deep learning models. More about this organization can be found in the Dataset Structure section.
      text:The annual ImageNet Large Scale Visual Recognition Challenge (ILSVRC) has been pivotal in driving advancements in computer vision by providing a competitive platform for evaluating algorithms on a large-scale, standardized dataset. It offers standardized evaluation metrics, fostering innovation and development in areas such as image classification, object detection, and image segmentation. The challenge has continuously pushed the boundaries of what is possible with deep learning and computer vision technologies.

Social Networks {👍}(4)

External Links {🔗}(33)

Analytics and Tracking {📊}

Google Analytics
Google Analytics 4
Google Tag Manager

Libraries {📚}

Clipboard.js

CDN Services {📦}

Cloudflare
Jsdelivr
Weglot

2.79s.