Update README with correct paths and usage examples

README.md

@@ -28,96 +28,37 @@ size_categories:
 source_datasets:
 - AI-EcoNet/HUGO-Bench
 configs:
-- config_name: cluster_count_prediction
-  data_files:
-  - split: train
-    path: 06_cluster_count_prediction/*.json
+- 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:
-  - split: train
-    path: 04_clustering_supervised/*.json
+  data_files: clustering_supervised/train-*.parquet
 - 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
+  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:
-  - 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
+  data_files: intra_species_variation/train-*.parquet
 - config_name: scaling_tests
-  data_files:
-  - split: train
-    path: scaling_tests/train-*
-- config_name: subsample_definitions
-  data_files:
-  - split: train
-    path: subsample_definitions/train-*
+  data_files: scaling_tests/train-*.parquet
 - 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
+  data_files: uneven_distribution/train-*.parquet
 - 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
+  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
@@ -125,187 +66,168 @@ dataset_info:
 **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.
+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.
 
-### Relationship to HUGO-Bench
+### Related Resources
 
-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:
+- **Source Images**: [AI-EcoNet/HUGO-Bench](https://huggingface.co/datasets/AI-EcoNet/HUGO-Bench) - 139,111 wildlife images
+- **Code Repository**: Coming soon
 
-- **Clustering results** from all 27,600 experimental configurations
-- **Pre-computed embeddings** enabling reproduction without image access
-- **Execution logs** for full experimental traceability
+## Repository Structure
 
-| 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 |
+```
+├── 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
+```
 
-## Repository Structure
+## 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'])}")
 ```
-├── 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
-    └── ...
+
+### 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'])}")
 ```
 
-## Key Results Summary
+### Load Specific Analysis Results
 
-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
+```python
+# Model comparison results
+model_comp = load_dataset("AI-EcoNet/HUGO-Bench-Paper-Reproducibility", "model_comparison")
 
-### Top Performing Configuration
+# Scaling test results
+scaling = load_dataset("AI-EcoNet/HUGO-Bench-Paper-Reproducibility", "scaling_tests")
 
-| 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 |
+# Intra-species variation analysis
+intra = load_dataset("AI-EcoNet/HUGO-Bench-Paper-Reproducibility", "intra_species_variation")
+```
 
-## Usage
+### Load Cluster Image Mappings
 
-### Loading Results with Python
+The intra-species analysis includes a mapping file showing which images belong to which clusters:
 
 ```python
-import pandas as pd
+from huggingface_hub import hf_hub_download
 import json
 
-# Load primary benchmarking results
-results = pd.read_csv("01_primary_benchmarking/clustering_analysis_complete.csv")
+# Download mapping file
+mapping_file = hf_hub_download(
+    "AI-EcoNet/HUGO-Bench-Paper-Reproducibility",
+    "intra_species_variation/cluster_image_mappings.json",
+    repo_type="dataset"
+)
 
-# Filter for best model
-dinov3_results = results[results['model'] == 'dinov3']
+with open(mapping_file) as f:
+    mappings = json.load(f)
 
-# Load JSON metrics
-with open("05_clustering_unsupervised/unsupervised_metrics_by_class.json") as f:
-    unsupervised = json.load(f)
+# Structure: {species: {run: {cluster: [image_names]}}}
+print(f"Species analyzed: {list(mappings.keys())}")
 ```
 
-### Using Pre-computed Embeddings
+### Load Full Dataset Embeddings
 
-The `10_embeddings/` folder contains pre-computed embeddings that allow running clustering experiments **without needing the original images**:
+For the extreme uneven distribution experiments, we provide full dataset embeddings:
 
 ```python
-import numpy as np
-import json
+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'])}")
+```
 
-# Load embeddings
-embeddings = np.load("10_embeddings/embeddings/dinov3_embeddings.npy")
+## Experimental Setup
 
-# Load corresponding image list
-with open("01_primary_benchmarking/images_run_1.json") as f:
-    image_list = json.load(f)
-```
+### Models Evaluated
 
-### Reproducing Paper Tables
+| 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 |
 
-Each folder corresponds to specific paper sections:
+### Clustering Methods
 
-| 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/` |
+- K-Means, DBSCAN, HDBSCAN, Agglomerative, Spectral
+- GMM (Gaussian Mixture Models)
+- With and without dimensionality reduction (UMAP, t-SNE, PCA)
 
-## File Formats
+### Evaluation Metrics
 
-| 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 |
+- **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 data, please cite both the paper and HUGO-Bench:
+If you use this dataset, please cite:
 
 ```bibtex
-@article{markoff2025vit_clustering,
+@article{markoff2026vision,
   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}
+  author={Markoff, Hugo and Bengtson, Stefan Hein and Ørsted, Michael},
+  journal={[Journal/Conference]},
+  year={2026}
 }
 ```
 
 ## License
 
-This dataset is released under [CC BY 4.0](https://creativecommons.org/licenses/by/4.0/).
+This dataset is released under the [CC-BY-4.0 License](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
+For questions or issues, please open an issue in this repository or contact the authors.