---
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**.