Data

ImageNet: Top-performing AI systems in labeling images

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What you should know about this indicator

  • The top-1 accuracy measure is used to assess how frequently a model's absolute top prediction matches the correct answer from a given set of options.
  • Here's an example to illustrate what this benchmark tests: Imagine an image classification model that is presented with an image of an animal. The model assigns probabilities to each potential label and generates its highest-confidence prediction. For instance, when analyzing an image, the model might predict 'Cat' as the most probable label. To evaluate the model's accuracy using the top-1 measure, researchers compare this prediction with the correct label. If the model's top prediction matches the correct label (e.g., if the actual animal in the image is indeed a cat), then the model's prediction is considered correct according to the top-1 accuracy metric. On the other hand, if the model's top prediction does not match the correct label (e.g., if the image shows a dog, but the model predicts a cat), then the model's prediction is considered incorrect based on the top-1 accuracy measure. To calculate the top-1 accuracy, researchers analyze the model's performance on a large dataset where the correct labels are known. They determine the percentage of examples in the dataset where the model's highest-confidence prediction matches the actual label.
  • This measure provides a focused evaluation of the model's ability to make accurate predictions by considering only its absolute top guess.
Source
Papers with Code (2024) – with major processing by Our World in Data
Last updated
July 23, 2024
Unit
%

Sources and processing

This data is based on the following sources

Retrieved on
July 23, 2024
Citation
This is the citation of the original data obtained from the source, prior to any processing or adaptation by Our World in Data. To cite data downloaded from this page, please use the suggested citation given in Reuse This Work below.
Image Classification on ImageNet. Papers with Code (2024)

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Citations

How to cite this page

To cite this page overall, including any descriptions, FAQs or explanations of the data authored by Our World in Data, please use the following citation:

“Data Page: ImageNet: Top-performing AI systems in labeling images”, part of the following publication: Charlie Giattino, Edouard Mathieu, Veronika Samborska and Max Roser (2023) - “Artificial Intelligence”. Data adapted from Papers with Code. Retrieved from https://ourworldindata.org/grapher/ai-performance-imagenet [online resource]
How to cite this data

In-line citationIf you have limited space (e.g. in data visualizations), you can use this abbreviated in-line citation:

Papers with Code (2024) – with major processing by Our World in Data

Full citation

Papers with Code (2024) – with major processing by Our World in Data. “ImageNet: Top-performing AI systems in labeling images” [dataset]. Papers with Code, “AI Performance on Imagenet” [original data]. Retrieved October 7, 2024 from https://ourworldindata.org/grapher/ai-performance-imagenet