STL10-Labeled

This public repo contains labels for the unlabeled pictures in the stl10 dataset.

Please refer to files Files and versions tab above.

You can also refer to my original repo https://github.com/semihyagli/STL10-Labeled

Please consider sponsoring this repo so that we can continue to develop high-quality datasets for the ML/AI research.

To become a sponsor:

GitHub Sponsors
Buy me a coffee

You can also sponsor us by downloading our free application, Etiqueta, to your devices:

Etiqueta on iOS or Apple Chip Macs
Etiqueta on Android

More information on the original STL-10 dataset can be found here: https://cs.stanford.edu/~acoates/stl10/
Thanks to Martin Tutek, the original STL-10 dataset can be downloaded via the python code here: https://github.com/mttk/STL10

If you use this dataset in your research please do not forget to cite:

@techreport{yagli2025etiqueta,
  author      = {Semih Yagli},
  title       = {Etiqueta: AI-Aided, Gamified Data Labeling to Label and Segment Data},
  year        = {2025},
  number      = {TR-2025-0001},
  address     = {NJ, USA},
  month       = Apr.,
  url         = {https://www.aidatalabel.com/technical_reports/aidatalabel_tr_2025_0001.pdf},
  institution = {AI Data Label},
}

@inproceedings{coates2011analysis,
  title     = {An analysis of single-layer networks in unsupervised feature learning},
  author    = {Coates, Adam and Ng, Andrew and Lee, Honglak},
  booktitle = {Proceedings of the fourteenth international conference on artificial intelligence and statistics},
  pages     = {215--223},
  year      = {2011},
  organization={JMLR Workshop and Conference Proceedings}
}

Note: If you notice any errors and/or if you have comments/ideas relevant to this dataset or Etiqueta in general, please reach me out at contact@aidatalabel.com.

Instructions

To download the dataset, run:

python stl10

1. This will create a folder named data download and extract the stl10 dataset inside that folder.
2. Then it will show one example picture in a new window.
3. Once you close this example picture, images will then be saved to disk under a folder called img

If you just want to load images as numpy.array:

import stl10
images = read_all_images(DATA_PATH_TO_BINARY_FILE)

where by default DATA_PATH_TO_BINARY_FILE can be either of the following:

• ./data/stl10_binary/test_X.bin
• ./data/stl10_binary/train_X.bin
• ./data/stl10_binary/unlabeled_X.bin

This repo specifically contains the labels for the images inside unlabeled_X.bin

Examples

Class	ai0 - passenger plane	ai1 - small plane	ai2 - sea plane	ai3 - military plane	ai4 - toy plane	ai5 - helicopter
airplane

Class	ai6 - hot air balloon	ai7 - zeppelin	ai8 - spaceship	ai9 - other balloon	ai10 - kite
airplane

Class	an0 - antelope	an1 - wildebeest	an2 - oryx	an3 - dikdik	an4 - eland	an5 - kudu
antelope

Class	an6 - gerenuk	an7 - springbok	an8 - hartebeest	an9 - waterbuck	an10 - blackbuck	an11 - gazelle
antelope

Class	an12 - impala	an13 - saiga	an14 - addax	an15 - kob	an16 - topi
antelope

Class	be0 - black bear	be1 - brown bear	be2 - polar bear	be3 - panda bear	be4 - red panda
bear

Class	bi0 - bird	bi1 - chicken	bi2 - duck
bird

Class	cam0 - camel	cam1 - lama/alpaca
camelid

Class	ca0 - dog	ca1 - fox	ca2 - wolf	ca3 - coyote	ca4 - jackal	ca5 - maned wolf	ca6 - dog sled
canine

Class	car0 - horse carriage	car1 - cattle carriage	car2 - human carriage	car3 - donkey carriage	car4 - goat carriage	car5 - other carriage
carriage

Class	cy0 - motorcycle	cy1 - monocycle	cy2 - bicycle	cy3 - tricycle	cy4 - snowmobile	cy5 - skateboard	cy6 - scooter
cycle

Class	eq0 - horse	eq1 - donkey	eq2 - onager	eq3 - zorse	eq4 - zebra	eq5 - okapi
equine

Class	fe0 - cat	fe1 - lion	fe2 - tiger	fe3 - leopard	fe4 - hyena	fe5 - meerkat
feline

Class	fe6 - fossa	fe7 - caracal	fe8 - lynx	fe9 - bearcat	fe10 - jaguar
feline

Class	fe11 - cougar	fe12 - civet	fe13 - mongoose	fe14 - genet	fe15 - kusimanse
feline

Class	mac0 - tractor	mac1 - excavator	mac2 - rolling compactor	mac3 - full-track tank	mac4 - half-track car	mac5 - forklift	mac6 - rocket
machinery

Class	mm0 - dolphin	mm1 - whale	mm2 - narwhal	mm3 - vaquita
marine mammals

Class	mm4 - dugong/manatee	mm5 - seal/sea lion	mm6 - walrus	mm7 - porpoise
marine mammals

Class	mu0 - badger	mu1 - weasel/stoat	mu2 - polecat	mu3 - ferret	mu4 - mink	mu5 - wolverine
mustelinae

Class	mu6 - tayra	mu7 - skunk	mu8 - otter	mu9 - marten	mu10 - fisher
mustelinae

Class	pri0 - monkey	pri1 - nightmonkey	pri2 - lemur	pri3 - tarsier	pri4 - gorilla	pri5 - mandrill	pri6 - orangutan
primate

Class	pri7 - proboscis	pri8 - tamarin	pri9 - colobus	pri10 - ape	pri11 - marmoset	pri12 - colugo
primate

Class	pr0 - raccoon	pr1 - ringtail	pr2 - coati	pr3 - kinkajou	pr4 - olinguito
procyonid

Class	re0 - snake	re1 - turtle	re2 - sea turtle	re3 - lizard	re4 - frilled-neck lizard
reptile

Class	re5 - monitor lizard	re6 - chameleon	re7 - aligator	re8 - iguana	re9 - gila monster
reptile

Class	ro0 - mouse	ro1 - rat	ro2 - hamster	ro3 - guinea pig	ro4 - squirrel	ro5 - rabbit
rodent

Class	ro6 - pika	ro7 - vole	ro8 - mouse-deer	ro9 - agouti	ro10 - jerboa	ro11 - marmot
rodent

Class	ro12 - nutria	ro13 - chinchilla	ro14 - shrew	ro15 - paca	ro16 - lemming	ro17 - hedgehog
rodent

Class	ru0 - deer	ru1 - pronghorn	ru2 - cattle	ru3 - sheep	ru4 - goat
ruminant

Class	ru5 - markhor	ru6 - giraffe	ru7 - takin	ru8 - serow	ru9 - goral
ruminant

Class	sw0 - pig	sw1 - boar/peccary	sw2 - warthog	sw3 - babirusa
swine

Class	tr0 - train	tr1 - tram
train

Class	tru0 - truck-general	tru1 - truck bobtail	tru2 - freight truck	tru3 - gravel truck	tru4 - small-box truck	tru5 - mini truck
truck

Class	tru6 - pickup truck	tru7 - car carrier/trailer	tru8 - garbage truck	tru9 - firetruck	tru10 - tow truck	tru11 - snowplow
truck

Class	ut0 - shopping cart	ut1 - dolly	ut2 - wheel barrow	ut3 - buffet trolley
utility

Class	ut4 - laundry cart	ut5 - stroller	ut6 - other utility	ut7 - sleigh
utility

Class	ve0 - car	ve1 - limo	ve2 - golf-cart	ve3 - 4x4	ve4 - bus	ve5 - minibus	ve6 - minivan
vehicle

Class	ve7 - van	ve8 - caravan	ve9 - bumper car	ve10 - go-kart	ve11 - amphibious	ve12 - soapbox car	ve13 - mini caravan
vehicle

Class	wa0 - wooden boat	wa1 - jet boat	wa2 - catamaran boat	wa3 - yacht	wa4 - jetski
watercraft

Class	wa5 - sail boat	wa6 - other boat	wa7 - sail ship	wa8 - cruise ship
watercraft

Class	wa9 - military ship	wa10 - cargo ship	wa11 - other ship	wa12 - submarine
watercraft

Class	om0 - bat	om1 - elephant	om2 - hyrax	om3 - hippo	om4 - rhino	om5 - tapir	om6 - anteater
other mammals

Class	om7 - armadillo	om8 - pangolin	om9 - aardvark	om10 - tasmanian devil	om11 - sloth	om12 - cuscus	om13 - possum
other mammals

Class	ou0 - restaurant	ou1 - human	ou2 - multiple subject	ou3 - unknown animal	ou4 - unclear picture	ou5 - skull
outlier

Class	ou6 - moth	ou7 - car-interior	ou8 - train-interior	ou9 - ship-interior	ou10 - japanese giant salamander	ou11 - tambaqui fish
outlier

Class	ou12 - seahorse	ou13 - frog	ou14 - kangaroo	ou15 - mantis
outlier

Notes - For future Relases

antelope in 0-55k will be redone
mouse in 0-35k needs to be redone
rat in 0-35k --> separate chincilla (needs to be redone)
cat in 0-35k --> separate civet and genet
cougar --> may be moved to lion, because of lack of clarity
fe15 until 75k, there should only be 1.
rocket separate two types of rocket into mchinery and aircraft...
mm1 separate orca from whales
sw1 separate peccaries
ve12 separate bobsled and soap box car
wa6 tugboat can be separated, also check for yacht