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---
size_categories:
- 10K<n<100K
task_categories:
- unconditional-image-generation
dataset_info:
features:
- name: label
dtype:
class_label:
names:
'0': 001.Black_footed_Albatross
'1': 002.Laysan_Albatross
'2': 003.Sooty_Albatross
'3': 004.Groove_billed_Ani
'4': 005.Crested_Auklet
'5': 006.Least_Auklet
'6': 007.Parakeet_Auklet
'7': 008.Rhinoceros_Auklet
'8': 009.Brewer_Blackbird
'9': 010.Red_winged_Blackbird
'10': 011.Rusty_Blackbird
'11': 012.Yellow_headed_Blackbird
'12': 013.Bobolink
'13': 014.Indigo_Bunting
'14': 015.Lazuli_Bunting
'15': 016.Painted_Bunting
'16': 017.Cardinal
'17': 018.Spotted_Catbird
'18': 019.Gray_Catbird
'19': 020.Yellow_breasted_Chat
'20': 021.Eastern_Towhee
'21': 022.Chuck_will_Widow
'22': 023.Brandt_Cormorant
'23': 024.Red_faced_Cormorant
'24': 025.Pelagic_Cormorant
'25': 026.Bronzed_Cowbird
'26': 027.Shiny_Cowbird
'27': 028.Brown_Creeper
'28': 029.American_Crow
'29': 030.Fish_Crow
'30': 031.Black_billed_Cuckoo
'31': 032.Mangrove_Cuckoo
'32': 033.Yellow_billed_Cuckoo
'33': 034.Gray_crowned_Rosy_Finch
'34': 035.Purple_Finch
'35': 036.Northern_Flicker
'36': 037.Acadian_Flycatcher
'37': 038.Great_Crested_Flycatcher
'38': 039.Least_Flycatcher
'39': 040.Olive_sided_Flycatcher
'40': 041.Scissor_tailed_Flycatcher
'41': 042.Vermilion_Flycatcher
'42': 043.Yellow_bellied_Flycatcher
'43': 044.Frigatebird
'44': 045.Northern_Fulmar
'45': 046.Gadwall
'46': 047.American_Goldfinch
'47': 048.European_Goldfinch
'48': 049.Boat_tailed_Grackle
'49': 050.Eared_Grebe
'50': 051.Horned_Grebe
'51': 052.Pied_billed_Grebe
'52': 053.Western_Grebe
'53': 054.Blue_Grosbeak
'54': 055.Evening_Grosbeak
'55': 056.Pine_Grosbeak
'56': 057.Rose_breasted_Grosbeak
'57': 058.Pigeon_Guillemot
'58': 059.California_Gull
'59': 060.Glaucous_winged_Gull
'60': 061.Heermann_Gull
'61': 062.Herring_Gull
'62': 063.Ivory_Gull
'63': 064.Ring_billed_Gull
'64': 065.Slaty_backed_Gull
'65': 066.Western_Gull
'66': 067.Anna_Hummingbird
'67': 068.Ruby_throated_Hummingbird
'68': 069.Rufous_Hummingbird
'69': 070.Green_Violetear
'70': 071.Long_tailed_Jaeger
'71': 072.Pomarine_Jaeger
'72': 073.Blue_Jay
'73': 074.Florida_Jay
'74': 075.Green_Jay
'75': 076.Dark_eyed_Junco
'76': 077.Tropical_Kingbird
'77': 078.Gray_Kingbird
'78': 079.Belted_Kingfisher
'79': 080.Green_Kingfisher
'80': 081.Pied_Kingfisher
'81': 082.Ringed_Kingfisher
'82': 083.White_breasted_Kingfisher
'83': 084.Red_legged_Kittiwake
'84': 085.Horned_Lark
'85': 086.Pacific_Loon
'86': 087.Mallard
'87': 088.Western_Meadowlark
'88': 089.Hooded_Merganser
'89': 090.Red_breasted_Merganser
'90': 091.Mockingbird
'91': 092.Nighthawk
'92': 093.Clark_Nutcracker
'93': 094.White_breasted_Nuthatch
'94': 095.Baltimore_Oriole
'95': 096.Hooded_Oriole
'96': 097.Orchard_Oriole
'97': 098.Scott_Oriole
'98': 099.Ovenbird
'99': 100.Brown_Pelican
'100': 101.White_Pelican
'101': 102.Western_Wood_Pewee
'102': 103.Sayornis
'103': 104.American_Pipit
'104': 105.Whip_poor_Will
'105': 106.Horned_Puffin
'106': 107.Common_Raven
'107': 108.White_necked_Raven
'108': 109.American_Redstart
'109': 110.Geococcyx
'110': 111.Loggerhead_Shrike
'111': 112.Great_Grey_Shrike
'112': 113.Baird_Sparrow
'113': 114.Black_throated_Sparrow
'114': 115.Brewer_Sparrow
'115': 116.Chipping_Sparrow
'116': 117.Clay_colored_Sparrow
'117': 118.House_Sparrow
'118': 119.Field_Sparrow
'119': 120.Fox_Sparrow
'120': 121.Grasshopper_Sparrow
'121': 122.Harris_Sparrow
'122': 123.Henslow_Sparrow
'123': 124.Le_Conte_Sparrow
'124': 125.Lincoln_Sparrow
'125': 126.Nelson_Sharp_tailed_Sparrow
'126': 127.Savannah_Sparrow
'127': 128.Seaside_Sparrow
'128': 129.Song_Sparrow
'129': 130.Tree_Sparrow
'130': 131.Vesper_Sparrow
'131': 132.White_crowned_Sparrow
'132': 133.White_throated_Sparrow
'133': 134.Cape_Glossy_Starling
'134': 135.Bank_Swallow
'135': 136.Barn_Swallow
'136': 137.Cliff_Swallow
'137': 138.Tree_Swallow
'138': 139.Scarlet_Tanager
'139': 140.Summer_Tanager
'140': 141.Artic_Tern
'141': 142.Black_Tern
'142': 143.Caspian_Tern
'143': 144.Common_Tern
'144': 145.Elegant_Tern
'145': 146.Forsters_Tern
'146': 147.Least_Tern
'147': 148.Green_tailed_Towhee
'148': 149.Brown_Thrasher
'149': 150.Sage_Thrasher
'150': 151.Black_capped_Vireo
'151': 152.Blue_headed_Vireo
'152': 153.Philadelphia_Vireo
'153': 154.Red_eyed_Vireo
'154': 155.Warbling_Vireo
'155': 156.White_eyed_Vireo
'156': 157.Yellow_throated_Vireo
'157': 158.Bay_breasted_Warbler
'158': 159.Black_and_white_Warbler
'159': 160.Black_throated_Blue_Warbler
'160': 161.Blue_winged_Warbler
'161': 162.Canada_Warbler
'162': 163.Cape_May_Warbler
'163': 164.Cerulean_Warbler
'164': 165.Chestnut_sided_Warbler
'165': 166.Golden_winged_Warbler
'166': 167.Hooded_Warbler
'167': 168.Kentucky_Warbler
'168': 169.Magnolia_Warbler
'169': 170.Mourning_Warbler
'170': 171.Myrtle_Warbler
'171': 172.Nashville_Warbler
'172': 173.Orange_crowned_Warbler
'173': 174.Palm_Warbler
'174': 175.Pine_Warbler
'175': 176.Prairie_Warbler
'176': 177.Prothonotary_Warbler
'177': 178.Swainson_Warbler
'178': 179.Tennessee_Warbler
'179': 180.Wilson_Warbler
'180': 181.Worm_eating_Warbler
'181': 182.Yellow_Warbler
'182': 183.Northern_Waterthrush
'183': 184.Louisiana_Waterthrush
'184': 185.Bohemian_Waxwing
'185': 186.Cedar_Waxwing
'186': 187.American_Three_toed_Woodpecker
'187': 188.Pileated_Woodpecker
'188': 189.Red_bellied_Woodpecker
'189': 190.Red_cockaded_Woodpecker
'190': 191.Red_headed_Woodpecker
'191': 192.Downy_Woodpecker
'192': 193.Bewick_Wren
'193': 194.Cactus_Wren
'194': 195.Carolina_Wren
'195': 196.House_Wren
'196': 197.Marsh_Wren
'197': 198.Rock_Wren
'198': 199.Winter_Wren
'199': 200.Common_Yellowthroat
- name: latent
sequence:
sequence:
sequence: float32
splits:
- name: train
num_bytes: 784939344
num_examples: 11788
download_size: 363204712
dataset_size: 784939344
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
# Dataset Card for cub2011-latent-64
This dataset includes the latent vectors calculated by:
1. CUB2011 images
2. Resample 512 x 512
3. Encode with vae = AutoencoderKL.from_pretrained("runwayml/stable-diffusion-v1-5", subfolder="vae", torch_dtype=torch.float16)
The output are 64x64 images with 4 channels.
## Dataset Details
When using it should be loaded as follows:
```
from diffusers import AutoencoderKL
import torch
vae = AutoencoderKL.from_pretrained("runwayml/stable-diffusion-v1-5", subfolder="vae", torch_dtype=torch.float16)
dataset.set_format('torch', columns=['latent'], output_all_columns=True)
```
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