SVHN
E431003
SVHN (Street View House Numbers) is a real-world image dataset of house number digits captured from Google Street View, commonly used for training and evaluating machine learning models in digit recognition tasks.
All labels observed (1)
| Label | Occurrences |
|---|---|
| SVHN canonical | 1 |
How this entity was disambiguated
This entity first appeared as the object of triple T4325994 — 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: SVHN Context triple: [torchvision, dataset, SVHN]
-
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-10
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.
-
C.
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.
-
D.
EMNIST
EMNIST is an extended handwritten character dataset that builds on MNIST by including both digits and letters for more comprehensive character recognition tasks.
-
E.
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.
- 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: SVHN Target entity description: SVHN (Street View House Numbers) is a real-world image dataset of house number digits captured from Google Street View, commonly used for training and evaluating machine learning models in digit recognition tasks.
-
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-10
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.
-
C.
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.
-
D.
EMNIST
EMNIST is an extended handwritten character dataset that builds on MNIST by including both digits and letters for more comprehensive character recognition tasks.
-
E.
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.
- F. None of above. chosen
Statements (46)
| Predicate | Object |
|---|---|
| instanceOf |
benchmark dataset
ⓘ
digit recognition dataset ⓘ image dataset ⓘ |
| accessMethod | download from official website ⓘ |
| acronymFor | Street View House Numbers NERFINISHED ⓘ |
| benchmarkStatus | widely used benchmark in computer vision research ⓘ |
| colorSpace | RGB ⓘ |
| commonUseCase |
domain adaptation research
ⓘ
evaluating deep learning models ⓘ semi-supervised learning research ⓘ testing convolutional neural networks ⓘ |
| contains |
cropped digit images
ⓘ
full number images ⓘ house number digits ⓘ |
| dataSource | Google Street View NERFINISHED ⓘ |
| dataType |
RGB images
ⓘ
natural images ⓘ |
| digitRange | 0-9 ⓘ |
| domain |
computer vision
ⓘ
machine learning ⓘ |
| extraSetSize | 531131 images (Format 2) ⓘ |
| formatVariant |
Format 1: full numbers with bounding boxes
ⓘ
Format 2: cropped digits ⓘ |
| fullName | Street View House Numbers NERFINISHED ⓘ |
| hasBackgroundClutter | true ⓘ |
| hasRealWorldNoise | true ⓘ |
| hasSplit |
extra training set
ⓘ
test set ⓘ training set ⓘ |
| hasVariableIllumination | true ⓘ |
| isRealWorldDataset | true ⓘ |
| isSyntheticDataset | false ⓘ |
| labelType |
bounding box annotations
ⓘ
digit class labels ⓘ |
| license | non-commercial research use ⓘ |
| moreChallengingThan | MNIST NERFINISHED ⓘ |
| resolutionTypical | 32x32 pixels ⓘ |
| similarTo | MNIST NERFINISHED ⓘ |
| taskType |
image classification
ⓘ
object detection ⓘ |
| testSetSize | 26032 images (Format 2) ⓘ |
| trainingSetSize | 73257 images (Format 2) ⓘ |
| usedFor |
benchmarking recognition algorithms
ⓘ
digit recognition ⓘ multi-digit number recognition ⓘ 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: SVHN Description of subject: SVHN (Street View House Numbers) is a real-world image dataset of house number digits captured from Google Street View, commonly used for training and evaluating machine learning models in digit recognition tasks.
Referenced by (1)
Full triples — surface form annotated when it differs from this entity's canonical label.
subject surface form:
torchvision