Datasets:

AI-EcoNet
/

HUGO-Bench

license: mit
task_categories:
  - image-classification
language:
  - en
tags:
  - wildlife
  - camera-trap
  - animals
  - vision
  - zero-shot
  - clustering
  - benchmark
  - HUGO-Bench
size_categories:
  - 100K<n<1M

HUGO-Bench

Hierarchical Unsupervised Grouping of Organisms Benchmark

A comprehensive benchmark dataset for evaluating zero-shot clustering of wildlife camera trap images using Vision Transformer embeddings.

Overview

HUGO-Bench contains 139,111 expert-validated cropped images of 60 animal species (30 birds, 30 mammals), derived from 23 camera trap projects across LILA BC. The dataset enables benchmarking of Vision Transformer models for unsupervised species-level clustering without requiring labeled training data.

Dataset Details

Property	Value
Format	Parquet (optimized for streaming)
Total Images	139,111
Species Count	60 species (30 birds, 30 mammals)
Validated	138,024 (99.2%)
Uncertain	1,087 (0.8%)
License	MIT

Splits

aves: 73,528 images of 30 bird species
mammals: 65,583 images of 30 mammal species

Columns

image: PIL Image object
label: Taxonomic class (Aves or Mammals)
filename: Original filename with species identifier (e.g., "american-crow_0001.jpg")

Species Organization

Filenames follow the pattern: {species-name}_{number}.jpg or uncertain_{species-name}_{number}.jpg

Species names use lowercase with hyphens (e.g., "american-crow", "red-fox")
"uncertain_" prefix indicates cases where manual validation was uncertain of the given class

Usage

Streaming (recommended for large datasets)

from datasets import load_dataset

# Load with streaming enabled
dataset = load_dataset("AI-EcoNet/HUGO-Bench", split="aves", streaming=True)

# Iterate through samples
for sample in dataset:
    image = sample['image']
    label = sample['label']
    filename = sample['filename']
    # Process your data

Filter by species (streaming)

from datasets import load_dataset

# Stream and filter for specific species
ds = load_dataset("AI-EcoNet/HUGO-Bench", split="aves", streaming=True)
crows_only = ds.filter(lambda x: x['filename'].startswith('american-crow'))

for sample in crows_only:
    print(sample['filename'])

Load specific subset

from datasets import load_dataset

# Load only first 1000 images from aves
dataset = load_dataset("AI-EcoNet/HUGO-Bench", split="aves[:1000]")

# Load 10% of mammals
dataset = load_dataset("AI-EcoNet/HUGO-Bench", split="mammals[:10%]")

Full download

from datasets import load_dataset

# Download full dataset
dataset = load_dataset("AI-EcoNet/HUGO-Bench")

# Access by split
aves_data = dataset['aves']
mammals_data = dataset['mammals']

Complete Species List

Aves (Birds) - 30 Species, 73,528 Images

Species	Total	Validated	Uncertain
American crow	1,240	1,234	6
Australasian swamphen	2,772	2,772	0
Australian magpie	2,739	2,733	6
Black curassow	1,949	1,939	10
Blue whistling thrush	2,157	2,128	29
Brown quail	2,662	2,652	10
Chicken	1,647	1,629	18
Common chaffinch	2,384	2,370	14
Common myna	2,707	2,704	3
Dunnock	2,066	2,055	11
European starling	1,876	1,867	9
Fantails	2,127	2,121	6
Greenfinch	2,503	2,483	20
Kea	2,833	2,814	19
Kiwi	1,634	1,620	14
Kori bustard	1,485	1,484	1
Mountain quail	2,381	2,337	44
New Zealand robin	2,753	2,719	34
Ostrich	3,727	3,722	5
Petrel	2,370	2,354	16
Pipit	2,316	2,238	78
Red junglefowl	2,684	2,671	13
Spix's guan	3,032	3,022	10
Swamp harrier	2,917	2,917	0
Takahē	2,085	2,073	12
Tūī	2,767	2,747	20
Vulturine guineafowl	4,420	4,404	16
Weka	2,421	2,407	14
Wild turkey	2,057	2,057	0
Yellow-eyed penguin	2,817	2,815	2

Mammals - 30 Species, 65,583 Images

Species	Total	Validated	Uncertain
Alpaca	1,864	1,864	0
American black bear	2,912	2,797	115
Black-backed jackal	2,067	2,053	14
Black rhinoceros	1,428	1,419	9
Bushpig	1,241	1,230	11
Common brushtail possum	1,365	1,358	7
Crab-eating mongoose	2,364	2,316	48
Crested porcupine	1,449	1,449	0
Dromedary camel	1,809	1,798	11
Eastern gray squirrel	2,434	2,414	20
Ferret badger	1,743	1,671	72
Gemsbok	3,963	3,944	19
Giant armadillo	463	461	2
Giraffe	2,985	2,974	11
Greater kudu	4,410	4,375	35
Hippopotamus	2,319	2,314	5
Jaguar	6,855	6,831	24
L'Hoest's monkey	2,941	2,921	20
Least weasel	1,869	1,869	0
Northern treeshrew	699	687	12
NZ sea lion	2,296	2,277	19
Raccoon	2,700	2,577	123
Serval	625	625	0
Ship rat	822	822	0
Spotted paca	1,715	1,673	42
Stump-tailed macaque	3,558	3,535	23
Sun bear	713	709	4
Warthog	1,671	1,666	5
White-nosed coati	1,996	1,982	14
Wolf	2,307	2,307	0

Note: "Uncertain" indicates cases where manual validation was uncertain of the initial label.

Citation

If you use HUGO-Bench, please cite:

@dataset{hugo_bench_2026,
  author       = {Markoff, Hugo and Bengtson, Stefan Hein and Ørsted, Michael},
  title        = {HUGO-Bench: Hierarchical Unsupervised Grouping of Organisms Benchmark},
  year         = {2026},
  publisher    = {Hugging Face Datasets},
  url          = {https://huggingface.co/datasets/AI-EcoNet/HUGO-Bench},
  note         = {Expert-validated camera-trap images for zero-shot clustering. Image sources from LILA BC.}
}

Acknowledgments

Raw image data derived from 23 projects on LILA BC (Labeled Information Library of Alexandria: Biology and Conservation). Please cite the original datasets as appropriate.