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Datasets:
Horama
/
wow_scraped

Tasks:
Image Classification
Languages:
English
French
Size:
100K<n<1M
Tags:
wildlife
animals
biology
zoology
inaturalist
flickr
License:
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Wildlife Scraped

Multi-source wildlife image dataset covering 122 labels (species, sub-species and a few domestic categories).

Each parquet shard is a HuggingFace Image feature so images decode straight to PIL.Image via load_dataset.

Quick stats

  • 139,231 images, 122 labels
  • last updated: 2026-04-29
  • sources: inaturalist, wikimedia, wikipedia, flickr, ddg

Splits (across all sources): train = 97,456 (70.0%) valid = 20,913 (15.0%) test = 20,862 (15.0%)

Source Images train valid test
inaturalist 121,851 85,288 18,279 18,284
wikimedia 7,692 5,386 1,176 1,130
wikipedia 0 0 0 0
flickr 0 0 0 0
ddg 9,688 6,782 1,458 1,448
TOTAL 139,231 97,456 20,913 20,862
Per-label image counts (122 labels, 139231 images) – click to expand
label specie inaturalist wikimedia wikipedia flickr ddg train valid test total
addax addax 746 54 0 0 0 561 119 120 800
agouti_azara agouti 2365 50 0 0 0 1690 363 362 2415
alpaga alpaga 523 74 0 0 0 419 89 89 597
amazone_aourou amazone 840 38 0 0 0 614 131 133 878
ane_commun ane 2000 68 0 0 0 1448 310 310 2068
ane_somalie ane 190 52 0 0 317 390 86 83 559
anoa anoa 99 46 0 0 340 340 72 73 485
antilope_cervicapre antilope 1485 70 0 0 0 1088 235 232 1555
autruche autruche 1000 76 0 0 0 753 162 161 1076
bison_amerique bison 2018 67 0 0 0 1465 310 310 2085
bison_europe bison 786 72 0 0 0 599 128 131 858
boeuf_ecosse boeuf 0 59 0 0 868 649 139 139 927
bongo bongo 257 53 0 0 344 465 95 94 654
cacatoes_rosalbin cacatoes 1465 75 0 0 0 1079 229 232 1540
calao_abyssinie calao 1159 81 0 0 0 868 186 186 1240
calopsitte calopsitte 1127 81 0 0 0 845 181 182 1208
canard canard 1000 78 0 0 0 754 162 162 1078
capybara capybara 2000 79 0 0 0 1455 306 318 2079
cariama_huppe cariama 1688 76 0 0 0 1236 265 263 1764
casoar casoar 1843 53 0 0 0 1324 292 280 1896
chat chat 1000 80 0 0 0 756 162 162 1080
cheval_comtois cheval 1000 63 0 0 0 744 161 158 1063
cheval_przewalski cheval 478 87 0 0 0 395 82 88 565
chevre_anglo_nubienne chevre 30 101 0 0 568 486 101 112 699
chevre_naine chevre 0 50 0 0 637 481 104 102 687
chien chien 1000 110 0 0 0 777 167 166 1110
chien_buisson chien_buisson 238 66 0 0 212 360 77 79 516
chouette_effraie chouette 2000 43 0 0 0 1430 306 307 2043
coati_nez_blanc coati 1000 75 0 0 0 765 155 155 1075
cobe_croissant cobe 1000 56 0 0 0 739 159 158 1056
cobe_lechwe cobe 627 86 0 0 0 498 109 106 713
cochon_d_inde cochon 587 77 0 0 0 463 98 103 664
cochon_laineux cochon 1000 92 0 0 322 991 212 211 1414
colobe_guereza colobe 2334 63 0 0 0 1678 360 359 2397
colombine_turvert colombine 1000 33 0 0 0 724 155 154 1033
corbeau corbeau 1000 73 0 0 0 751 161 161 1073
coyote coyote 1000 95 0 0 0 767 164 164 1095
daim_europe daim 2000 71 0 0 0 1450 311 310 2071
dhole dhole 1134 70 0 0 477 1176 255 250 1681
dromadaire dromadaire 871 77 0 0 0 664 143 141 948
eland_cap eland 2404 83 0 0 0 1741 374 372 2487
elephant_afrique elephant 1000 56 0 0 0 739 159 158 1056
emeu emeu 1000 86 0 0 0 761 163 162 1086
fourmilier fourmilier 939 81 0 0 0 715 152 153 1020
gazelle_perse gazelle 1232 82 0 0 0 920 196 198 1314
gibbon_bonnet gibbon 329 45 0 0 254 437 93 98 628
girafe_kordofan girafe 1189 56 0 0 374 1133 244 242 1619
gnou_bleu gnou 1000 69 0 0 0 746 167 156 1069
gnou_queue_blanche gnou 1183 71 0 0 0 873 199 182 1254
gorille_plaines gorille 712 71 0 0 0 549 117 117 783
grand_eclectus eclectus 384 24 0 0 299 495 106 106 707
grand_hocco hocco 754 28 0 0 0 548 117 117 782
grand_koudou koudou 2000 74 0 0 0 1452 312 310 2074
grue_couronnee_grise grue 1114 30 0 0 0 797 169 178 1144
guepard guepard 1000 83 0 0 0 762 161 160 1083
hapalemur_lac hapalemur 918 2 0 0 0 638 132 150 920
hippopotame hippopotame 2000 92 0 0 0 1464 314 314 2092
hippotrague_noir hippotrague 1345 90 0 0 0 1007 215 213 1435
hocco_daubenton hocco 33 1 0 0 239 191 41 41 273
hyene_tachetee hyene 1000 60 0 0 0 742 159 159 1060
kangourou_roux kangourou 1000 54 0 0 0 738 158 158 1054
lapin_geant_papillon lapin 2000 50 0 0 0 1437 308 305 2050
lemur_couronne lemur 563 12 0 0 0 401 87 87 575
lemur_noir lemur 875 83 0 0 0 671 144 143 958
lemur_ventre_rouge lemur 640 22 0 0 0 463 100 99 662
lion_afrique lion 1000 58 0 0 0 740 161 157 1058
loriquet_arc_en_ciel loriquet 1000 85 0 0 0 759 162 164 1085
loup_arctique loup 296 47 0 0 396 516 111 112 739
loup_criniere loup_criniere 891 84 0 0 0 683 146 146 975
loup_mackenzie loup 348 30 0 0 285 460 107 96 663
loutre_asie loutre 1710 73 0 0 0 1247 266 270 1783
lynx_carpates lynx 2057 64 0 0 0 1486 318 317 2121
macaque_tonkean macaque_noir 1159 49 0 0 223 999 221 211 1431
mangabey_dore mangabey 859 86 0 0 207 807 173 172 1152
maki_catta maki 1358 80 0 0 0 1006 217 215 1438
maki_vari_noir maki 1119 54 0 0 0 821 176 176 1173
maki_vari_roux maki 842 31 0 0 0 617 129 127 873
mara mara 1408 80 0 0 0 1043 223 222 1488
milan_noir milan 1000 100 0 0 0 769 164 167 1100
moufette moufette 1000 66 0 0 0 746 161 159 1066
mouton_somalie mouton 1000 55 0 0 615 1170 250 250 1670
mouton_valachie mouton 0 12 0 0 609 434 94 93 621
muntjac_chine muntjac 2000 49 0 0 0 1434 308 307 2049
nandou nandou 1000 48 0 0 0 736 156 156 1048
oie oie 1000 73 0 0 0 753 160 160 1073
oryx_algazelle oryx 1196 74 0 0 0 889 190 191 1270
oryx_arabie oryx 852 79 0 0 0 651 140 140 931
ouistiti_pygmee ouistiti 1097 76 0 0 0 818 178 177 1173
ours_baribal ours 1132 69 0 0 0 840 180 181 1201
ours_lunettes ours 491 73 0 0 0 393 84 87 564
panda_roux panda 611 62 0 0 0 473 100 100 673
panthere_chine panthere 525 8 0 0 246 545 117 117 779
panthere_neige panthere 471 53 0 0 288 572 122 118 812
patas patas 1292 70 0 0 0 953 205 204 1362
pelican_blanc pelican 1000 64 0 0 0 745 160 159 1064
perruche_patagonie perruche 541 73 0 0 0 427 91 96 614
phacochere phacochere 1000 63 0 0 0 742 164 157 1063
pigeon_madagascar pigeon 976 11 0 0 0 691 148 148 987
raton_laveur raton_laveur 1000 70 0 0 0 749 160 161 1070
renard_polaire renard 1000 83 0 0 0 758 166 159 1083
rhinoceros_blanc rhinoceros 1000 81 0 0 0 759 162 160 1081
saimiri_perou saimiri 2034 68 0 0 0 1474 314 314 2102
saki_face_blanche saki 1206 62 0 0 0 889 190 189 1268
serval serval 455 54 0 0 227 518 109 109 736
sitatunga sitatunga 319 48 0 0 286 457 96 100 653
springbok springbok 647 71 0 0 0 500 115 103 718
suricate suricate 478 73 0 0 0 385 82 84 551
tamarin_goeldi tamarin 249 47 0 0 490 548 121 117 786
tamarin_lion tamarin 407 5 0 0 276 477 102 109 688
tamarin_pinche tamarin 1288 92 0 0 0 964 210 206 1380
tamarin_roux tamarin 655 51 0 0 0 492 101 113 706
tapir_terrestre tapir 833 70 0 0 0 632 135 136 903
tigre_siberie tigre 1102 80 0 0 0 826 178 178 1182
titi_roux titi 513 0 0 0 289 561 121 120 802
urubu_tete_rouge urubu 1000 81 0 0 0 757 162 162 1081
vanneau_soldat vanneau 1000 36 0 0 0 725 157 154 1036
vautour_fauve vautour 1000 86 0 0 0 762 162 162 1086
vautour_ruppell vautour 1234 10 0 0 0 870 186 188 1244
vigogne vigogne 1000 94 0 0 0 766 164 164 1094
wallaby_bennett wallaby 1000 79 0 0 0 755 162 162 1079
watusi watusi 1000 101 0 0 0 770 170 161 1101
zebre_chapman zebre 696 76 0 0 0 540 117 115 772
TOTAL 121851 7692 0 0 9688 97456 20913 20862 139231

Porto subset

A curated selection for the Porto zoo project, tracked separately in <src>/porto_metadata.csv. Same schema as the main metadata.csv. The subset contains 42,125 images / 39 labels — most rows overlap with the main metadata, but a few labels are exclusive to porto (not in thoiry_labels.json): aigle_royal, ane_miranda, buffle_eau_asiatique, cerf_philippines, cochon_vietnamien, faisan_dore, girafe, iguane_vert, lapin_domestique, macaque_ours, nyala, pigeon_couronne_occidental, tamarin_empereur, vautour_charognard.

The full label list is available at porto_labels.json.

Per-label image counts (porto subset) – click to expand
label specie inaturalist wikimedia ddg train valid test total
agouti_azara agouti 3093 100 0 2235 478 480 3193
aigle_royal aigle 10 10 0 16 3 1 20
ane_miranda ane 2010 78 0 1462 312 314 2088
antilope_cervicapre antilope 1485 70 0 1088 235 232 1555
autruche autruche 1000 76 0 752 165 159 1076
buffle_eau_asiatique buffle 10 9 0 13 3 3 19
capybara capybara 2000 79 0 1456 312 311 2079
cerf_philippines cerf 10 2 0 8 3 1 12
cheval_comtois cheval 1000 63 0 744 160 159 1063
chevre_naine chevre 0 50 637 481 104 102 687
cobe_lechwe cobe 627 86 0 499 107 107 713
cochon_d_inde cochon 587 77 0 465 100 99 664
cochon_vietnamien cochon 1000 92 322 990 212 212 1414
emeu emeu 1000 86 0 760 163 163 1086
faisan_dore faisan 10 10 0 13 5 2 20
girafe girafe 3189 112 374 2577 549 549 3675
gnou_queue_blanche gnou 1820 141 0 1373 295 293 1961
guepard guepard 1000 83 0 758 163 162 1083
hyene_tachetee hyene 1000 60 0 742 159 159 1060
iguane_vert iguane 10 9 0 15 3 1 19
lapin_domestique lapin 2000 50 0 1435 312 303 2050
loutre_asie loutre 1727 142 0 1307 281 281 1869
macaque_ours macaque 10 9 0 12 3 4 19
maki_catta maki 2358 158 0 1759 374 383 2516
maki_vari_roux maki 846 62 0 636 136 136 908
nyala nyala 10 10 0 13 4 3 20
panda_roux panda 611 62 0 472 101 100 673
phacochere phacochere 1000 63 0 745 160 158 1063
pigeon_couronne_occidental pigeon 10 10 0 14 4 2 20
pigeon_madagascar pigeon 992 22 0 710 151 153 1014
rhinoceros_blanc rhinoceros 1000 81 0 757 163 161 1081
suricate suricate 478 73 0 385 83 83 551
tamarin_empereur tamarin 10 10 0 14 4 2 20
tamarin_pinche tamarin 2288 183 0 1730 371 370 2471
tapir_terrestre tapir 833 70 0 632 136 135 903
tigre_siberie tigre 1108 160 0 886 189 193 1268
vautour_charognard vautour 10 9 0 13 4 2 19
vigogne vigogne 1000 94 0 766 164 164 1094
wallaby_bennett wallaby 1000 79 0 756 162 161 1079
TOTAL 38152 2640 1333 29489 6333 6303 42125

Sources

Source Folder Code Default size
iNaturalist inaturalist/ ina medium (500 px)
Wikimedia Commons wikimedia/ wkm medium (500 px)
Wikipedia wikipedia/ wkp medium (500 px)
Flickr flickr/ fli medium (500 px)
DuckDuckGo ddg/ ddg medium (500 px)

Filtering

  • iNaturalist – only Alive annotations (term 17 / value 18); quality grades research, needs_id, casual; wild and captive both kept (recorded in inat_savage). Image sizes restricted to small (240 px) or medium (500 px).
  • Flickr / Wikimedia / Wikipedia / DDG – negative-keyword title/URL filter (dead, carcass, skull, bones, taxidermy, trophy, hunt, feces, dung, scat, slaughter, roadkill, …).
  • DDG + Wikipedia – inline CLIP alive vs not-alive classification (openai/clip-vit-base-patch32); rejected URLs are persisted in <src>/rejected_urls/<worker>.csv so they are skipped on subsequent runs.
  • Relevance filter (post-hoc, scraping.relevance_filter) – species-aware CLIP pass on already-pushed shards: drops rows that fail either an alive threshold or a species-relevance threshold (positive prompts built from the english / scientific / sub-species names). Dropped URLs are appended to <src>/rejected_urls/post_filter.csv so subsequent scrapes skip them. Typical use: cleaning landscapes, prey, or off-species photos from older DDG / Wikipedia shards.

Repository layout 

Horama/wow_scraped/
├── inaturalist/
│   ├── 0-000.parquet               # worker 0, shard 0 (~500 MB)
│   ├── 0-001.parquet
│   ├── 3-000.parquet               # worker 3, shard 0
│   ├── …
│   ├── metadata.csv                # consolidated metadata
│   └── metadata/
│       ├── 0-000.csv               # per-shard metadata (raw)
│       └── …
├── wikimedia/  …
├── wikipedia/  …                   # also contains rejected_urls/
├── flickr/     …
└── ddg/        …                   # also contains rejected_urls/

Schema

Each parquet shard:

image:        struct<bytes: binary, path: string>   # HF Image feature
filename:     string    # src3_VV_W_NNNNNN.ext (e.g. ina_00_3_000042.jpg)
label:        string    # canonical label (e.g. loup_arctique)
specie:       string    # broader group (e.g. loup)
sub_specie:   string    # = label (full granularity)
src:          string    # inaturalist | wikimedia | wikipedia | flickr | ddg
licence:      string    # CC0, CC-BY, ..., unknown
author:       string
url:          string    # original source URL
resolution:   string    # "WxH"
split:        string    # train | valid | test (added by scraping.add_split_column)
inat_obs:     string    # iNaturalist observation id (other sources: "")
location:     string    # "lat,lon" when available, else ""
inat_quality: string    # research | needs_id | casual (iNat only)
inat_savage:  string    # "savage" | "captive" (iNat only)

Loading 

from datasets import load_dataset

# All shards from one source
ds = load_dataset("Horama/wow_scraped", data_dir="inaturalist", split="train")
print(ds[0]["image"])          # PIL.Image.Image
print(ds[0]["label"], ds[0]["licence"])

# A single shard
ds = load_dataset(
    "Horama/wow_scraped",
    data_files={"train": "inaturalist/0-000.parquet"},
    split="train",
)

Filtering by split

The split assignment (train / valid / test) lives in the per-source metadata.csv only — the parquets themselves don't carry it (so you keep the freedom to re-split without re-uploading the images). Join the metadata to filter:

import csv
from datasets import load_dataset
from huggingface_hub import hf_hub_download

split_lookup: dict[str, str] = {}
csv_path = hf_hub_download("Horama/wow_scraped", repo_type="dataset",
                            filename="inaturalist/metadata.csv")
with open(csv_path) as f:
    for row in csv.DictReader(f):
        split_lookup[row["filename"]] = row["split"]

ds = load_dataset("Horama/wow_scraped", data_dir="inaturalist", split="train")
train = ds.filter(lambda r: split_lookup.get(r["filename"]) == "train")
valid = ds.filter(lambda r: split_lookup.get(r["filename"]) == "valid")
test  = ds.filter(lambda r: split_lookup.get(r["filename"]) == "test")

iNaturalist observations are kept whole (no inat_obs is split between train/valid/test).

Licensing

Per-image license is preserved verbatim in the licence column so you can filter / partition the dataset for either research or production use.

Common values Allowed for
CC0, Public Domain Mark 1.0, No known copyright restrictions, US Government Work any (incl. commercial)
CC BY, CC BY-SA any with attribution
CC BY-NC, CC BY-NC-SA, CC BY-NC-ND, cc-by-nc research only
unknown (DDG) research only, attribution undetermined

Reproducing the dataset

The full pipeline (workers, sharding, alive filter, mailer) lives at Horama/WOW_dataset_creation under scraping/.

Citation

If you use this dataset, please credit Horama and the underlying contributors of each photo (see licence and author columns).
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