README.md

---
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](https://lila.science/). 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)
```python
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)
```python
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
```python
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
```python
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:

```bibtex
@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.}
}
```

## Links

-  [Interactive Demo](https://hugomarkoff.github.io/animal_visual_transformer/)
-  Paper: *Vision Transformers for Zero-Shot Clustering of Animal Images: A Comparative Benchmarking Study*

## Acknowledgments

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