CIFAR-10
E367298
CIFAR-10 is a widely used computer vision dataset of 60,000 labeled low-resolution images across 10 object classes, commonly employed to benchmark image classification algorithms.
All labels observed (3)
| Label | Occurrences |
|---|---|
| CIFAR-10 canonical | 3 |
| CIFAR-10 dataset | 3 |
| CIFAR10 | 1 |
How this entity was disambiguated
This entity first appeared as the object of triple T3542984 — resolving that mention is where its identity was fixed. The disambiguator weighed these candidate entities and picked the highlighted one (or “None”, minting a new entity). This is how homonymy is resolved: the same surface form can point to different entities.
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: CIFAR-10 Context triple: [ResNet, benchmarkedOn, CIFAR-10]
-
A.
MNIST
MNIST is a widely used benchmark dataset of handwritten digit images commonly employed for training and evaluating image classification algorithms in machine learning and computer vision.
-
B.
CIFAR
CIFAR (the Canadian Institute for Advanced Research) is a Canadian global research organization that supports long-term, collaborative, interdisciplinary research, including major initiatives in artificial intelligence.
-
C.
Fashion-MNIST
Fashion-MNIST is a popular benchmark dataset of Zalando clothing item images used as a more challenging drop-in replacement for the original MNIST handwritten digits in machine learning research.
-
D.
KMNIST
KMNIST is a benchmark image dataset of handwritten Japanese characters (hiragana) designed as a more complex, drop-in replacement for the original MNIST digit dataset.
-
E.
ImageNet
ImageNet is a large-scale visual database widely used for training and benchmarking image classification and computer vision algorithms.
- F. None of above. chosen
- G. Unsure - the case is ambiguous/there is not enough information to decide.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: CIFAR-10 Target entity description: CIFAR-10 is a widely used computer vision dataset of 60,000 labeled low-resolution images across 10 object classes, commonly employed to benchmark image classification algorithms.
-
A.
MNIST
MNIST is a widely used benchmark dataset of handwritten digit images commonly employed for training and evaluating image classification algorithms in machine learning and computer vision.
-
B.
CIFAR
CIFAR (the Canadian Institute for Advanced Research) is a Canadian global research organization that supports long-term, collaborative, interdisciplinary research, including major initiatives in artificial intelligence.
-
C.
Fashion-MNIST
Fashion-MNIST is a popular benchmark dataset of Zalando clothing item images used as a more challenging drop-in replacement for the original MNIST handwritten digits in machine learning research.
-
D.
KMNIST
KMNIST is a benchmark image dataset of handwritten Japanese characters (hiragana) designed as a more complex, drop-in replacement for the original MNIST digit dataset.
-
E.
ImageNet
ImageNet is a large-scale visual database widely used for training and benchmarking image classification and computer vision algorithms.
- F. None of above. chosen
Statements (52)
| Predicate | Object |
|---|---|
| instanceOf |
benchmark dataset
ⓘ
computer vision dataset ⓘ image classification dataset ⓘ |
| baselineModelType | convolutional neural network ⓘ |
| classType | mutually exclusive ⓘ |
| commonlyUsedWith | convolutional neural networks ⓘ |
| containsTestSet | true ⓘ |
| containsTrainingSet | true ⓘ |
| dataFormat |
Python pickled files
ⓘ
binary batch files ⓘ |
| developedAt | University of Toronto ⓘ |
| developedBy |
Alex Krizhevsky
ⓘ
Geoffrey Hinton ⓘ |
| difficultyLevel | moderate ⓘ |
| domain | natural images ⓘ |
| downloadURL | https://www.cs.toronto.edu/~kriz/cifar.html ⓘ |
| hasClass |
airplane
ⓘ
automobile ⓘ bird ⓘ cat ⓘ deer ⓘ dog ⓘ frog ⓘ horse ⓘ ship ⓘ truck ⓘ |
| hasColorChannels | 3 ⓘ |
| hasImageHeight | 32 ⓘ |
| hasImagesPerClass | 6000 ⓘ |
| hasImageWidth | 32 ⓘ |
| hasNumberOfClasses | 10 ⓘ |
| hasNumberOfImages | 60000 ⓘ |
| hasNumberOfTestImages | 10000 ⓘ |
| hasNumberOfTrainingImages | 50000 ⓘ |
| hasTestImagesPerClass | 1000 ⓘ |
| hasTrainingImagesPerClass | 5000 ⓘ |
| imageSource | tiny images dataset ⓘ |
| imageType | RGB ⓘ |
| introducedInYear | 2009 ⓘ |
| isBalanced | true ⓘ |
| isPubliclyAvailable | true ⓘ |
| labelType | single-label ⓘ |
| license | MIT-like license ⓘ |
| partOf |
CIFAR datasets
ⓘ
linked to:
CIFAR
|
| predecessor |
Tiny Images dataset
ⓘ
linked to:
tiny images dataset
|
| publishedBy | Canadian Institute for Advanced Research ⓘ |
| resolution | 32x32 pixels ⓘ |
| typicalSplit | 50000 training / 10000 test ⓘ |
| usedFor |
benchmarking deep learning models
ⓘ
benchmarking machine learning algorithms ⓘ image classification ⓘ supervised learning ⓘ |
How these facts were elicited
The pipeline generated the facts above by prompting gpt-5.1 with this entity's name + description and the instruction below.
Instruction
You are a knowledge base construction expert. Given a subject entity and a description of it, return factual statements that you know for the subject as a JSON list of dictionaries(triples), where keys must be "subject", "predicate" and "object". The number of facts may be very high, between 25 to 50 or more, for very popular subjects. For less popular subjects, the number of facts can be very low, like 5 or 10. # Requirements - If you don't know the subject at all, return an empty list. - If the subject is not a named entity, return an empty list. - Include at least one triple where predicate is "instanceOf". - Do not get too wordy. - Separate several objects into multiple triples with one object.
Input
Subject: CIFAR-10 Description of subject: CIFAR-10 is a widely used computer vision dataset of 60,000 labeled low-resolution images across 10 object classes, commonly employed to benchmark image classification algorithms.
Referenced by (7)
Full triples — surface form annotated when it differs from this entity's canonical label.
linked to: CIFAR-10
linked to: CIFAR-10
linked to: CIFAR-10