Update README.md

README.md

@@ -1,233 +1,233 @@
----
-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.
+---
+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** - https://arxiv.org/abs/2602.03894
+>
+> 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.