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: 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
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  - split: train
    path: scaling_tests/train-*
- config_name: subsample_definitions
  data_files:
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    path: subsample_definitions/train-*
- config_name: uneven_distribution
  data_files:
  - split: train
    path: uneven_distribution/train-*
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  - name: content
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  download_size: 11487
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  - name: content
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- config_name: uneven_distribution
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    dtype: string
  - name: content
    dtype: string
  splits:
  - name: train
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    num_examples: 410
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  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](https://huggingface.co/datasets/AI-EcoNet/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](https://huggingface.co/datasets/AI-EcoNet/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

```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**:

```python

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:

```bibtex

@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](https://creativecommons.org/licenses/by/4.0/).

## Contact

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

## Related Resources

- 📊 [HUGO-Bench Dataset](https://huggingface.co/datasets/AI-EcoNet/HUGO-Bench) - Source images (139,111 validated crops)
- 💻 [GitHub Repository](https://github.com/HugoMarkoff/animal_visual_transformer) - Code and scripts
- 🌐 [Interactive Visualization](https://hugomarkoff.github.io/animal_visual_transformer/) - Explore clustering results