README.md

---
license: mit
dataset_info:
  features:
  - name: image
    dtype: image
  - name: objects
    sequence:
    - name: bbox
      sequence: float64
    - name: category
      dtype: string
  splits:
  - name: train
    num_bytes: 5584254504.0
    num_examples: 2031
  download_size: 5575309514
  dataset_size: 5584254504.0
configs:
- config_name: default
  data_files:
  - split: train
    path: data/train-*
---

# Dataset Card for IBBI Bark Beetle Testing Dataset

## Dataset Summary

This dataset is the primary testing and benchmarking set for the `ibbi` Python package. It contains images of bark and ambrosia beetles used to evaluate the performance of object detection and classification models.

**Note**: While this dataset serves as the **testing set** for the `ibbi` package's evaluation functions, it is hosted on the Hugging Face Hub as the `train` split. You can access it using `ibbi.get_dataset(split='train')`.

### Dataset Summary

The Intelligent Bark Beetle Identifier (IBBI) testing dataset is a curated collection of 2,031 images of bark and ambrosia beetles, covering 63 distinct species. It was developed to provide a standardized benchmark for evaluating the performance of computer vision models on the challenging task of beetle identification. The dataset is specifically designed for evaluating both localization (finding the beetle) and classification (identifying the species) tasks. All images have been annotated by experts.

### Supported Tasks and Leaderboards

This dataset is primarily used for evaluating the following tasks within the `ibbi` package:

* **Object Detection**: Models are evaluated on their ability to accurately draw bounding boxes around beetles. The primary metric is mean Average Precision (mAP).
* **Object Classification**: For models that identify species, performance is measured using metrics like F1-score, accuracy, precision, and recall.
* **Embedding Quality**: The dataset is used to evaluate the quality of feature embeddings generated by models, assessing how well they separate different species in a high-dimensional space using clustering metrics.

### Dataset Structure

#### Data Instances

A typical data instance consists of an image and a corresponding set of object annotations.

```python
{
  'image': <PIL.Image.Image image>,
  'objects': {
    'bbox': [[217.0, 181.0, 526.0, 631.0]],
    'category': ['Xylosandrus_crassiusculus']
  }
}
````

#### Data Fields

  * `image`: A PIL Image object containing the image of a beetle specimen.
  * `objects`: A dictionary containing annotation information.
      * `bbox`: A list of bounding boxes, where each box is in `[x_1, y_1, x_2, y_2]` format.
      * `category`: A list of string labels corresponding to the species of the beetle in each bounding box. There are **63 unique species categories** in the dataset.

#### Data Splits

The dataset contains a single split, which is used for testing and evaluation. Although named `train` on the Hugging Face Hub, it functions as the official test set for the `ibbi` package.

  * **Testing set (`train` split)**: 2,031 images.

## Dataset Statistics

| Metric                         | Value         |
| ------------------------------ | ------------- |
| Total Images                   | 2,031         |
| Unique Species                 | 63            |
| Total Bounding Box Annotations | 16,480        |
| Average Bboxes per Image       | 8.11          |
| Average Image Dimensions (WxH) | 2211 x 1891   |

### Species Distribution

#### Total Object (Annotation) Count per Class
 Xylosandrus_compactus       : 1136 objects    
 Euwallacea_fornicatus       : 854 objects    
 Phloeosinus_dentatus        : 770 objects    
 Pityophthorus_juglandis     : 745 objects    
 Xyleborus_affinis           : 739 objects    
 Hylesinus_varius            : 725 objects    
 Xyleborinus_saxesenii       : 637 objects    
 Xyleborus_glabratus         : 556 objects    
 Ips_typographus             : 539 objects    
 Coccotrypes_dactyliperda    : 531 objects    
 Monarthrum_fasciatum        : 521 objects    
 Cryptocarenus_heveae        : 502 objects    
 Platypus_cylindrus          : 480 objects    
 Coccotrypes_carpophagus     : 421 objects    
 Scolytodes_glaber           : 417 objects    
 Xylosandrus_crassiusculus   : 399 objects    
 Euwallacea_perbrevis        : 391 objects    
 Dendroctonus_valens         : 344 objects    
 Ips_duplicatus              : 277 objects    
 Platypus_koryoensis         : 264 objects    
 Ips_acuminatus              : 244 objects    
 Ctonoxylon_hagedorn         : 239 objects    
 Coptoborus_ricini           : 239 objects    
 Euplatypus_compositus       : 237 objects    
 Dendroctonus_terebrans      : 235 objects    
 Ips_sexdentatus             : 228 objects    
 Pagiocerus_frontalis        : 225 objects    
 Hypothenemus_hampei         : 224 objects    
 Pycnarthrum_hispidium       : 214 objects    
 Hylurgops_palliatus         : 207 objects    
 Monarthrum_mali             : 199 objects    
 Anisandrus_sayi             : 195 objects    
 Ips_avulsus                 : 193 objects    
 Myoplatypus_flavicornis     : 189 objects    
 Hylesinus_toranio           : 181 objects    
 Xyleborus_ferrugineus       : 177 objects    
 Cnestus_mutilatus           : 166 objects    
 Ips_calligraphus            : 151 objects    
 Orthotomicus_erosus         : 129 objects    
 Hylastes_salebrosus         : 127 objects    
 Xylosandrus_morigerus       : 124 objects    
 Hylurgus_ligniperda         : 122 objects    
 Taphrorychus_bicolor        : 111 objects    
 Hylastes_porculus           : 104 objects    
 Tomicus_destruens           : 100 objects    
 Anisandrus_dispar           : 96 objects    
 Pityogenes_chalcographus    : 96 objects    
 Cyclorhipidion_pelliculosum : 88 objects    
 Hylesinus_crenatus          : 66 objects    
 Scolytus_schevyrewi         : 61 objects    
 Xylosandrus_amputatus       : 61 objects    
 Xylosandrus_germanus        : 50 objects    
 Ambrosiophilus_atratus      : 41 objects    
 Trypodendron_domesticum     : 16 objects    
 Xyleborus_celsus            : 15 objects    
 Hylesinus_aculeatus         : 13 objects    
 Dryocoetes_autographus      : 12 objects    
 Ambrosiodmus_minor          : 11 objects    
 Dendroctonus_rufipennis     : 10 objects    
 Scolytus_multistriatus      : 9 objects    
 Orthotomicus_caelatus       : 9 objects    
 Ips_grandicollis            : 9 objects    
 Euwallacea_validus          : 9 objects    

#### Unique Image Count per Class

 Ambrosiodmus_minor          : 11 images    
 Ambrosiophilus_atratus      : 11 images    
 Anisandrus_dispar           : 12 images    
 Anisandrus_sayi             : 7 images    
 Cnestus_mutilatus           : 23 images    
 Coccotrypes_carpophagus     : 8 images    
 Coccotrypes_dactyliperda    : 30 images    
 Coptoborus_ricini           : 10 images    
 Cryptocarenus_heveae        : 16 images    
 Ctonoxylon_hagedorn         : 12 images    
 Cyclorhipidion_pelliculosum : 7 images    
 Dendroctonus_rufipennis     : 9 images    
 Dendroctonus_terebrans      : 46 images    
 Dendroctonus_valens         : 264 images    
 Dryocoetes_autographus      : 8 images    
 Euplatypus_compositus       : 23 images    
 Euwallacea_fornicatus       : 38 images    
 Euwallacea_perbrevis        : 9 images    
 Euwallacea_validus          : 9 images    
 Hylastes_porculus           : 8 images    
 Hylastes_salebrosus         : 13 images    
 Hylesinus_aculeatus         : 13 images    
 Hylesinus_crenatus          : 9 images    
 Hylesinus_toranio           : 13 images    
 Hylesinus_varius            : 78 images    
 Hylurgops_palliatus         : 15 images    
 Hylurgus_ligniperda         : 25 images    
 Hypothenemus_hampei         : 7 images    
 Ips_acuminatus              : 145 images    
 Ips_avulsus                 : 11 images    
 Ips_calligraphus            : 18 images    
 Ips_duplicatus              : 14 images    
 Ips_grandicollis            : 9 images    
 Ips_sexdentatus             : 227 images    
 Ips_typographus             : 314 images    
 Monarthrum_fasciatum        : 42 images    
 Monarthrum_mali             : 14 images    
 Myoplatypus_flavicornis     : 19 images    
 Orthotomicus_caelatus       : 9 images    
 Orthotomicus_erosus         : 7 images    
 Pagiocerus_frontalis        : 11 images    
 Phloeosinus_dentatus        : 38 images    
 Pityogenes_chalcographus    : 11 images    
 Pityophthorus_juglandis     : 22 images    
 Platypus_cylindrus          : 43 images    
 Platypus_koryoensis         : 26 images    
 Pycnarthrum_hispidium       : 11 images    
 Scolytodes_glaber           : 14 images    
 Scolytus_multistriatus      : 8 images    
 Scolytus_schevyrewi         : 8 images    
 Taphrorychus_bicolor        : 9 images    
 Tomicus_destruens           : 9 images    
 Trypodendron_domesticum     : 12 images    
 Xyleborinus_saxesenii       : 41 images    
 Xyleborus_affinis           : 29 images    
 Xyleborus_celsus            : 15 images    
 Xyleborus_ferrugineus       : 13 images    
 Xyleborus_glabratus         : 18 images    
 Xylosandrus_amputatus       : 10 images    
 Xylosandrus_compactus       : 38 images    
 Xylosandrus_crassiusculus   : 60 images    
 Xylosandrus_germanus        : 11 images    
 Xylosandrus_morigerus       : 11 images    

## Dataset Creation

#### Curation Rationale

The dataset was created to address the need for a standardized, high-quality benchmark for automated bark beetle identification, a task traditionally reliant on expert taxonomists. The selection of 63 species provides a taxonomically diverse set for robust model evaluation.

#### Source Data

Images were collected from a variety of sources by the Forest Entomology Lab at the University of Florida to ensure diversity in lighting, background, and specimen condition.

#### Annotations

The annotation process involved a human-in-the-loop workflow:

1.  A zero-shot detection model was used to perform initial localization of beetles in the images.
2.  These initial bounding box annotations were then manually verified and corrected by human annotators to ensure accuracy.
3.  Species-level classification for each verified bounding box was provided by expert taxonomists to guarantee high-quality labels.

## Citation Information

If you use this dataset in your research, please cite the associated paper:

```bibtex
@article{marais2025progress,
  title={Progress in developing a bark beetle identification tool},
  author={Marais, G Christopher and Stratton, Isabelle C and Johnson, Andrew J and Hulcr, Jiri},
  journal={PLoS One},
  volume={20},
  number={6},
  pages={e0310716},
  year={2025},
  publisher={Public Library of Science San Francisco, CA USA}
}
```