README.md

metadata

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: cluster_count_prediction
    data_files:
      - split: train
        path: 06_cluster_count_prediction/*.json
  - config_name: clustering_supervised
    data_files:
      - split: train
        path: 04_clustering_supervised/*.json
  - config_name: clustering_unsupervised
    data_files:
      - split: train
        path: 05_clustering_unsupervised/*.json
  - config_name: dimensionality_reduction
    data_files:
      - split: train
        path: 03_dimensionality_reduction/*.json
  - config_name: intra_species_variation
    data_files:
      - split: train
        path: intra_species_variation/train-*
  - config_name: model_comparison
    data_files:
      - split: train
        path: 02_model_comparison/*.json
  - config_name: primary_benchmarking
    data_files:
      - split: train
        path: 01_primary_benchmarking/*.csv
    default: true
  - config_name: scaling_tests
    data_files:
      - split: train
        path: scaling_tests/train-*
  - config_name: subsample_definitions
    data_files:
      - split: train
        path: subsample_definitions/train-*
  - config_name: uneven_distribution
    data_files:
      - split: train
        path: uneven_distribution/train-*
dataset_info:
  - config_name: intra_species_variation
    features:
      - name: filename
        dtype: string
      - name: content
        dtype: string
    splits:
      - name: train
        num_bytes: 64315
        num_examples: 11
    download_size: 11487
    dataset_size: 64315
  - config_name: scaling_tests
    features:
      - name: filename
        dtype: string
      - name: content
        dtype: string
    splits:
      - name: train
        num_bytes: 5754770
        num_examples: 1205
    download_size: 1304695
    dataset_size: 5754770
  - config_name: subsample_definitions
    features:
      - name: filename
        dtype: string
      - name: content
        dtype: string
    splits:
      - name: train
        num_bytes: 3038864
        num_examples: 10
    download_size: 643403
    dataset_size: 3038864
  - config_name: uneven_distribution
    features:
      - name: filename
        dtype: string
      - name: content
        dtype: string
    splits:
      - name: train
        num_bytes: 1914245
        num_examples: 410
    download_size: 374649
    dataset_size: 1914245

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 species-level clustering of camera trap images.

Relationship to HUGO-Bench

This dataset is derived from HUGO-Bench, which provides the source images and species annotations. While HUGO-Bench contains the validated image crops (139,111 images across 60 species), this repository provides:

• Clustering results from all 27,600 experimental configurations
• Pre-computed embeddings enabling reproduction without image access
• Execution logs for full experimental traceability

Dataset	Content	Purpose
HUGO-Bench	139,111 validated camera trap images	Source images for experiments
This repository	Results, embeddings, logs	Paper reproducibility

Repository Structure

├── 01_primary_benchmarking/     # Full 27,600 configuration results
│   ├── clustering_analysis_complete.csv
│   ├── clustering_analysis_with_ami.csv
│   ├── comprehensive_vmeasure_by_class.json
│   └── images_run_*.json        # Subsample definitions (10 runs)
│
├── 02_model_comparison/         # 5 ViT model comparison
│   ├── dinov3_all_combinations_results.json
│   ├── dinov3_bioclip_siglip_all_methods_results.json
│   └── dinov3_comparison_results.json
│
├── 03_dimensionality_reduction/ # t-SNE, UMAP, PCA, Isomap, KPCA
│   └── dimensionality_comparison.json
│
├── 04_clustering_supervised/    # K-variation experiments (K=15,30,45,90,180)
│   ├── k30_metrics_by_class.json
│   └── k_variation_by_dimred_class.json
│
├── 05_clustering_unsupervised/  # HDBSCAN vs DBSCAN
│   └── unsupervised_metrics_by_class.json
│
├── 06_cluster_count_prediction/ # Progressive species testing (1,200 runs)
│   ├── progressive_species_testing_results.json
│   └── progressive_species_testing_results_expanded.json
│
├── 07_intra_species_variation/  # Age, sex, pelage detection
│   ├── wolf_dbscan_clusters/
│   └── intra_cluster/
│
├── 08_uneven_distribution/      # Long-tailed distribution tests
│   ├── extreme_20_max_test/
│   ├── original_config_extreme_uneven_test/
│   └── even_distribution_results.json
│
├── 09_scaling_tests/            # 5-60 species scaling behavior
│   ├── scaling_test_results/
│   └── different_n_test/
│
├── 10_embeddings/               # Pre-computed embeddings
│   ├── embeddings/              # Standard benchmarking embeddings
│   ├── extreme_uneven_embeddings/
│   └── extreme_uneven_image_lists/
│
└── execution_logs/              # Complete execution logs
    ├── clustering_dimred_log.txt
    ├── clustering_complete_log.txt
    └── ...

Key Results Summary

Our benchmarking evaluated 27,600 configurations across:

• 5 ViT Models: DINOv3, DINOv2, BioCLIP 2, CLIP, SigLIP
• 5 Dimensionality Reduction: t-SNE, UMAP, PCA, Isomap, Kernel PCA
• 4 Clustering Algorithms: Hierarchical, GMM, HDBSCAN, DBSCAN
• 60 Species: 30 mammals + 30 birds from camera trap imagery

Top Performing Configuration

Component	Best Choice	V-Measure
Model	DINOv3	0.958
Dim. Reduction	t-SNE	+26-38pp vs others
Clustering (supervised)	Hierarchical K=30	0.958
Clustering (unsupervised)	HDBSCAN	0.943

Usage

Loading Results with Python

import pandas as pd
import json

# Load primary benchmarking results
results = pd.read_csv("01_primary_benchmarking/clustering_analysis_complete.csv")

# Filter for best model
dinov3_results = results[results['model'] == 'dinov3']

# Load JSON metrics
with open("05_clustering_unsupervised/unsupervised_metrics_by_class.json") as f:
    unsupervised = json.load(f)

Using Pre-computed Embeddings

The 10_embeddings/ folder contains pre-computed embeddings that allow running clustering experiments without needing the original images:

import numpy as np
import json

# Load embeddings
embeddings = np.load("10_embeddings/embeddings/dinov3_embeddings.npy")

# Load corresponding image list
with open("01_primary_benchmarking/images_run_1.json") as f:
    image_list = json.load(f)

Reproducing Paper Tables

Each folder corresponds to specific paper sections:

Paper Section	Data Folder
Table 3 (V-measure by model)	01_primary_benchmarking/
Table 4 (Dim. reduction comparison)	03_dimensionality_reduction/
Table 5 (Supervised K variation)	04_clustering_supervised/
Table 6 (Unsupervised comparison)	05_clustering_unsupervised/
Figure 5 (Cluster count prediction)	06_cluster_count_prediction/
Table 7 (Intra-species traits)	07_intra_species_variation/
Table 8 (Uneven distribution)	08_uneven_distribution/
Figure 8 (Scaling behavior)	09_scaling_tests/

File Formats

Extension	Description	How to Load
.csv	Tabular results	pandas.read_csv()
.json	Structured metrics	json.load()
.npy	NumPy embeddings	numpy.load()
.txt/.log	Execution logs	Plain text

Citation

If you use this data, please cite both the paper and HUGO-Bench:

@article{markoff2025vit_clustering,
  title={Vision Transformers for Zero-Shot Clustering of Animal Images: A Comparative Benchmarking Study},
  author={Markoff, Hugo and Bengtson, Stefan Hein and {\O}rsted, Michael},
  journal={TBD},
  year={2025}
}

@dataset{hugo_bench,
  title={HUGO-Bench: A Benchmark Dataset for Camera Trap Species Clustering},
  author={AI-EcoNet},
  year={2025},
  url={https://huggingface.co/datasets/AI-EcoNet/HUGO-Bench}
}

License

This dataset is released under CC BY 4.0.

Contact

• Hugo Markoff - khbm@bio.aau.dk
• Department of Chemistry and Bioscience, Aalborg University

Related Resources

• 📊 HUGO-Bench Dataset - Source images (139,111 validated crops)
• 💻 GitHub Repository - Code and scripts
• 🌐 Interactive Visualization - Explore clustering results