--- 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': , '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} } ```