Fashion-MNIST
E363689
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.
All labels observed (3)
| Label | Occurrences |
|---|---|
| Fashion-MNIST canonical | 1 |
| Fashion-MNIST dataset | 1 |
| FashionMNIST | 1 |
How this entity was disambiguated
This entity first appeared as the object of triple T3507215 — 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: Fashion-MNIST Context triple: [MNIST, inspiredDataset, Fashion-MNIST]
-
A.
Miu Miu
Miu Miu is an Italian high-fashion brand known for its playful, avant-garde take on womenswear and accessories.
-
B.
Pashons
Pashons is the ninth month of the Coptic calendar, traditionally associated with the harvest season in Egypt.
-
C.
Freakum Dress
"Freakum Dress" is an uptempo R&B/hip-hop song by Beyoncé that celebrates confidence and empowerment through fashion and attitude.
-
D.
Seaborn Cotton
Seaborn Cotton was a 17th-century New England Puritan minister and the son of prominent theologian John Cotton.
-
E.
Outfit
Outfit is a common nickname for the Chicago Outfit, a powerful Italian-American organized crime syndicate historically based in Chicago.
- 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: Fashion-MNIST Target entity description: 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.
-
A.
Miu Miu
Miu Miu is an Italian high-fashion brand known for its playful, avant-garde take on womenswear and accessories.
-
B.
Pashons
Pashons is the ninth month of the Coptic calendar, traditionally associated with the harvest season in Egypt.
-
C.
Freakum Dress
"Freakum Dress" is an uptempo R&B/hip-hop song by Beyoncé that celebrates confidence and empowerment through fashion and attitude.
-
D.
Seaborn Cotton
Seaborn Cotton was a 17th-century New England Puritan minister and the son of prominent theologian John Cotton.
-
E.
Outfit
Outfit is a common nickname for the Chicago Outfit, a powerful Italian-American organized crime syndicate historically based in Chicago.
- F. None of above. chosen
Statements (53)
| Predicate | Object |
|---|---|
| instanceOf |
benchmark dataset
ⓘ
image dataset ⓘ machine learning dataset ⓘ |
| background | uniform ⓘ |
| classLabel |
Ankle boot
ⓘ
Bag ⓘ Coat ⓘ Dress ⓘ Pullover ⓘ Sandal ⓘ Shirt ⓘ Sneaker ⓘ T-shirt/top ⓘ Trouser ⓘ |
| colorSpace | grayscale ⓘ |
| contains | centered clothing item images ⓘ |
| dataModality | vision ⓘ |
| dataType | grayscale images ⓘ |
| designedAs | drop-in replacement for MNIST ⓘ |
| developer | Zalando Research ⓘ |
| domain |
clothing
ⓘ
fashion ⓘ |
| fileFormat | IDX ⓘ |
| hostedOn |
GitHub
ⓘ
Zalando research website ⓘ |
| imageChannels | 1 ⓘ |
| imageHeight | 28 ⓘ |
| imageWidth | 28 ⓘ |
| inputType | 2D image ⓘ |
| intendedUse |
benchmarking
ⓘ
education ⓘ research ⓘ |
| introducedAs | more realistic alternative to MNIST ⓘ |
| labelType | clothing category ⓘ |
| language | language independent ⓘ |
| license | MIT License ⓘ |
| moreChallengingThan | MNIST ⓘ |
| numberOfClasses | 10 ⓘ |
| numberOfTestExamples | 10000 ⓘ |
| numberOfTrainingExamples | 60000 ⓘ |
| outputType | discrete label ⓘ |
| popularity | widely used in machine learning literature ⓘ |
| preprocessing | normalized pixel intensities 0-255 ⓘ |
| publisher | Zalando Research ⓘ |
| resolution | 28x28 pixels ⓘ |
| similarTo | MNIST ⓘ |
| task |
multiclass classification
ⓘ
supervised learning ⓘ |
| totalNumberOfExamples | 70000 ⓘ |
| typicalSplit | 60000 train / 10000 test ⓘ |
| usedFor |
benchmarking machine learning algorithms
ⓘ
deep learning experiments ⓘ image classification research ⓘ |
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: Fashion-MNIST Description of subject: 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.
Referenced by (3)
Full triples — surface form annotated when it differs from this entity's canonical label.
subject surface form:
torchvision
this entity surface form:
FashionMNIST
this entity surface form:
Fashion-MNIST dataset