| | --- |
| | license: mit |
| | tags: |
| | - bird-detection |
| | - ecology |
| | - remote-sensing |
| | - computer-vision |
| | datasets: |
| | - DOI |
| | --- |
| | |
| | # Drone Bird Detector |
| |
|
| | Trained model for detecting and identifying birds in drone imagery, accompanying: |
| |
|
| | **Wilson, J. P., T. Amano, T. Bregnballe, A. Corregidor-Castro, R. Francis, D. Gallego-García, et al. (2026). Big Bird: A global dataset of birds in drone imagery annotated to species level. Remote Sensing in Ecology and Conservation. https://doi.org/10.1002/rse2.70059** |
| |
|
| | Trained on: |
| |
|
| | Insert Dataset DOI. |
| |
|
| | --- |
| |
|
| | ## Model Overview |
| |
|
| | This model performs **object detection and fine‑grained classification** of birds in aerial drone images. It predicts hierarchical labels including: |
| |
|
| | * Unique label ID |
| | * Taxonomic information: class → order → family → genus → species |
| | * Traits: age category (chick, juvenile, adult), sex (male, female, monomorphic) |
| | * Combined into a full label like: |
| |
|
| | ``` |
| | 1;aves;procellariiformes;diomedeidae;thalassarche;melanophris;adult;monomorphic;black-browed albatross - adult - monomorphic |
| | ``` |
| |
|
| | Training Images: high‑resolution drone RGB imagery. |
| | Model Type: Faster RCNN with resnet-101 backbone. |
| |
|
| | --- |
| |
|
| | ## Intended Use |
| |
|
| | **Primary use cases** |
| |
|
| | * Automated processing of large drone image collections for ecological surveys |
| | * Generating species counts and spatial patterns from aerial surveys |
| | * Supporting conservation monitoring and research workflows |
| |
|
| | **Not intended for** |
| |
|
| | * Real‑time inference on live video streams |
| | * Predicting species not present in the training dataset |
| | * Making decisions without ecological validation |
| |
|
| | **Limitations & Ethical Considerations** |
| |
|
| | This model reflects biases in the training data (e.g., species representation, habitat contexts). Users should: |
| |
|
| | * Validate outputs with domain experts |
| | * Be cautious of false positives in complex scenes |
| | * Understand that model performance may vary with altitude, sensor quality, and lighting |
| |
|
| | --- |
| |
|
| | ## Evaluation |
| |
|
| | Model performance metrics (precision/recall, mAP, per‑class accuracy, etc.) were reported in **Wilson et al. (2026)**. Users are encouraged to evaluate the model on their own images and share results. |
| |
|
| | --- |
| |
|
| | ## How to Download |
| |
|
| | You can use the `huggingface_hub` Python client to download model weights and labels: |
| |
|
| | ```python |
| | from huggingface_hub import hf_hub_download |
| | |
| | # Download model weights |
| | model_path = hf_hub_download( |
| | repo_id="JoshuaWilson/drone-bird-detector", |
| | filename="model.pth" |
| | ) |
| | |
| | # Download label definitions |
| | labels_path = hf_hub_download( |
| | repo_id="JoshuaWilson/drone-bird-detector", |
| | filename="labels.txt" |
| | ) |
| | ``` |
| |
|
| | --- |
| |
|
| | ## Citation |
| |
|
| | Please cite the original paper when using this model: |
| |
|
| | ```bibtex |
| | @article{Wilson2026, |
| | title={Big Bird: A global dataset of birds in drone imagery annotated to species level}, |
| | author={Joshua P. Wilson and Tatsuya Amano and Thomas Bregnballe and Alejandro Corregidor-Castro and Roxane Francis and {Diego Gallego-García} and Jarrod C. Hodgson and Landon R. Jones and {César R. Luque-Fernández} and Dominik Marchowski and John McEvoy and Ann E. McKellar and W. Chris Oosthuizen and Christian Pfeifer and Martin Renner and {José Hernán Sarasola} and {Mateo Sokač} and Roberto Valle and Adam Zbyryt and Richard A. Fuller}, |
| | journal={Remote Sensing in Ecology and Conservation}, |
| | year={2026}, |
| | doi={10.1002/rse2.70059} |
| | } |
| | ``` |
| |
|
| | --- |
| |
|
| | ## License |
| |
|
| | This model is released under the **MIT License**. |
| |
|
