ImageNet: Top-performing AI systems in labeling images
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.
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Citations
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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 DataFull 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 November 18, 2024 from https://ourworldindata.org/grapher/ai-performance-imagenet