README.md

---

license: cc-by-4.0
task_categories:
- image-classification
- zero-shot-classification
tags:
- biology
- ecology
- wildlife
- camera-traps
- vision-transformers
- clustering
- zero-shot-learning
- biodiversity
- reproducibility
- benchmarking
- embeddings
- dinov3
- dinov2
- bioclip
- clip
- siglip
language:
- en
pretty_name: HUGO-Bench Paper Reproducibility Data
size_categories:
- 100K<n<1M
source_datasets:
- AI-EcoNet/HUGO-Bench
configs:
- config_name: primary_benchmarking
  data_files: primary_benchmarking/train-*.parquet
  default: true
- config_name: model_comparison
  data_files: model_comparison/train-*.parquet
- config_name: dimensionality_reduction
  data_files: dimensionality_reduction/train-*.parquet
- config_name: clustering_supervised
  data_files: clustering_supervised/train-*.parquet
- config_name: clustering_unsupervised
  data_files: clustering_unsupervised/train-*.parquet
- config_name: cluster_count_prediction
  data_files: cluster_count_prediction/train-*.parquet
- config_name: intra_species_variation
  data_files: intra_species_variation/train-*.parquet
- config_name: scaling_tests
  data_files: scaling_tests/train-*.parquet
- config_name: uneven_distribution
  data_files: uneven_distribution/train-*.parquet
- config_name: subsample_definitions
  data_files: subsample_definitions/train-*.parquet
- config_name: embeddings_dinov3_vith16plus
  data_files: embeddings_dinov3_vith16plus/train-*.parquet
- config_name: embeddings_dinov2_vitg14
  data_files: embeddings_dinov2_vitg14/train-*.parquet
- config_name: embeddings_bioclip2_vitl14
  data_files: embeddings_bioclip2_vitl14/train-*.parquet
- config_name: embeddings_clip_vitl14
  data_files: embeddings_clip_vitl14/train-*.parquet
- config_name: embeddings_siglip_vitb16
  data_files: embeddings_siglip_vitb16/train-*.parquet
---


# HUGO-Bench Paper Reproducibility

**Supplementary data and reproducibility materials for the paper:**

> **Vision Transformers for Zero-Shot Clustering of Animal Images: A Comparative Benchmarking Study**
>

> Hugo Markoff, Stefan Hein Bengtson, Michael Ørsted
>

> Aalborg University, Denmark

## Dataset Description

This repository contains complete experimental results, pre-computed embeddings, and execution logs from our comprehensive benchmarking study evaluating Vision Transformer models for zero-shot clustering of wildlife camera trap images.

### Related Resources

- **Source Images**: [AI-EcoNet/HUGO-Bench](https://huggingface.co/datasets/AI-EcoNet/HUGO-Bench) - 139,111 wildlife images
- **Code Repository**: Coming soon

## Repository Structure

```

├── primary_benchmarking/          # Main benchmark results (27,600 configurations)

├── model_comparison/              # Cross-model comparisons

├── dimensionality_reduction/      # UMAP/t-SNE/PCA analysis

├── clustering_supervised/         # Supervised clustering metrics

├── clustering_unsupervised/       # Unsupervised clustering results

├── cluster_count_prediction/      # Optimal cluster count analysis

├── intra_species_variation/       # Within-species cluster analysis

│   ├── train-*.parquet           # Analysis results

│   └── cluster_image_mappings.json  # Image-to-cluster assignments

├── scaling_tests/                 # Sample size scaling experiments

├── uneven_distribution/           # Class imbalance experiments

├── subsample_definitions/         # Reproducible subsample definitions

├── embeddings_*/                  # Pre-computed embeddings (5 models)

│   ├── embeddings_dinov3_vith16plus/  # 120K embeddings, 1280-dim

│   ├── embeddings_dinov2_vitg14/      # 120K embeddings, 1536-dim

│   ├── embeddings_bioclip2_vitl14/    # 120K embeddings, 768-dim

│   ├── embeddings_clip_vitl14/        # 120K embeddings, 768-dim

│   └── embeddings_siglip_vitb16/      # 120K embeddings, 768-dim

├── extreme_uneven_embeddings/     # Full dataset embeddings (PKL)

│   ├── aves_full_dinov3_embeddings.pkl      # 74,396 embeddings

│   └── mammalia_full_dinov3_embeddings.pkl  # 65,484 embeddings

└── execution_logs/                # Experiment execution logs

```

## Quick Start

### Load Primary Benchmark Results

```python

from datasets import load_dataset



# Load main benchmark results (27,600 configurations)

ds = load_dataset("AI-EcoNet/HUGO-Bench-Paper-Reproducibility", "primary_benchmarking")

print(f"Configurations: {len(ds['train'])}")

```

### Load Pre-computed Embeddings

```python

# Load DINOv3 embeddings (120,000 images)

embeddings = load_dataset(

    "AI-EcoNet/HUGO-Bench-Paper-Reproducibility", 

    "embeddings_dinov3_vith16plus"

)

print(f"Embeddings shape: {len(embeddings['train'])} x {len(embeddings['train'][0]['embedding'])}")

```

### Load Specific Analysis Results

```python

# Model comparison results

model_comp = load_dataset("AI-EcoNet/HUGO-Bench-Paper-Reproducibility", "model_comparison")



# Scaling test results

scaling = load_dataset("AI-EcoNet/HUGO-Bench-Paper-Reproducibility", "scaling_tests")



# Intra-species variation analysis

intra = load_dataset("AI-EcoNet/HUGO-Bench-Paper-Reproducibility", "intra_species_variation")

```

### Load Cluster Image Mappings

The intra-species analysis includes a mapping file showing which images belong to which clusters:

```python

from huggingface_hub import hf_hub_download

import json



# Download mapping file

mapping_file = hf_hub_download(

    "AI-EcoNet/HUGO-Bench-Paper-Reproducibility",

    "intra_species_variation/cluster_image_mappings.json",

    repo_type="dataset"

)



with open(mapping_file) as f:

    mappings = json.load(f)



# Structure: {species: {run: {cluster: [image_names]}}}

print(f"Species analyzed: {list(mappings.keys())}")

```

### Load Full Dataset Embeddings

For the extreme uneven distribution experiments, we provide full dataset embeddings:

```python

from huggingface_hub import hf_hub_download

import pickle



# Download Aves embeddings (74,396 images)

pkl_file = hf_hub_download(

    "AI-EcoNet/HUGO-Bench-Paper-Reproducibility",

    "extreme_uneven_embeddings/aves_full_dinov3_embeddings.pkl",

    repo_type="dataset"

)



with open(pkl_file, 'rb') as f:

    data = pickle.load(f)



print(f"Embeddings: {data['embeddings'].shape}")  # (74396, 1280)

print(f"Labels: {len(data['labels'])}")

print(f"Paths: {len(data['paths'])}")

```

## Experimental Setup

### Models Evaluated

| Model | Architecture | Embedding Dim | Pre-training |
|-------|-------------|---------------|--------------|
| DINOv3 | ViT-H/16+ | 1280 | Self-supervised |
| DINOv2 | ViT-G/14 | 1536 | Self-supervised |
| BioCLIP 2 | ViT-L/14 | 768 | Biology domain |
| CLIP | ViT-L/14 | 768 | Contrastive |
| SigLIP | ViT-B/16 | 768 | Sigmoid loss |

### Clustering Methods

- K-Means, DBSCAN, HDBSCAN, Agglomerative, Spectral
- GMM (Gaussian Mixture Models)
- With and without dimensionality reduction (UMAP, t-SNE, PCA)

### Evaluation Metrics

- **Supervised**: Adjusted Rand Index (ARI), Normalized Mutual Information (NMI), Accuracy, F1
- **Unsupervised**: Silhouette Score, Calinski-Harabasz Index, Davies-Bouldin Index

## Citation

If you use this dataset, please cite:

```bibtex

@article{markoff2026vision,

  title={Vision Transformers for Zero-Shot Clustering of Animal Images: A Comparative Benchmarking Study},

  author={Markoff, Hugo and Bengtson, Stefan Hein and Ørsted, Michael},

  journal={[Journal/Conference]},

  year={2026}

}

```

## License

This dataset is released under the [CC-BY-4.0 License](https://creativecommons.org/licenses/by/4.0/).

## Contact

For questions or issues, please open an issue in this repository or contact the authors.