---
license: other
license_name: bsg-bird-model-license
license_link: >-
  https://raw.githubusercontent.com/plauha/BSG_classifier_builder/refs/heads/main/BSG%20-%20Bird%20Model%20License
tags:
- birdnet
- bioacustics
- audio
- luomus
- birds
- finland
---
# BSG Finnish Birds Model – ONNX Optimized

Optimized [ONNX](https://onnx.ai/) conversions of the **BSG – Finnish Birds Model**, a pretrained bird sound classification model fine-tuned for Finland. The original model was developed at the **University of Jyväskylä** and is based on the [BirdNET](https://github.com/kahst/BirdNET-Analyzer) model architecture.

These are **fused models** that combine BirdNET's EfficientNet-B0 feature extractor with the BSG classifier head into a single ONNX graph, making them directly compatible with standard BirdNET inference pipelines — no separate feature extraction step required.

## Model Description

The BSG Finnish Birds Model uses an **EfficientNet-B0** backbone (from BirdNET-Analyzer) with a custom classification head trained on vocalizations of **263 Finnish bird species**, covering all breeders, non-breeding migrants, and most common vagrants.

The original BSG model is distributed as a standalone classifier that expects pre-extracted BirdNET embeddings as input. The fused ONNX models in this repository merge BirdNET's feature extractor and the BSG classifier into a single end-to-end model that accepts raw spectrograms and outputs species predictions — identical to how the standard BirdNET ONNX model operates. This makes the BSG model a **drop-in replacement** for BirdNET in any application that supports custom ONNX models.

**Key characteristics:**

- Processes audio in **3-second overlapping segments** (spectrogram-based)
- Outputs species-wise detection probabilities, calibrated per species via logistic regression
- Predictions are filtered by species' seasonal and geographic plausibility to reduce misclassifications
- Compatible with standard BirdNET inference workflows (same input/output interface)

## Files

| File | Description | Recommended Use |
|------|-------------|-----------------|
| `BSG_birds_Finland_v4_4_fused_fp32.onnx` | Full precision (FP32) | GPU (CUDA/TensorRT), Desktop CPU |
| `BSG_birds_Finland_v4_4_labels_fi.txt` | Labels (Scientific name_Finnish name) | Class index mapping |

## ONNX Optimization

The ONNX models were converted from the original TFLite model and optimized for efficient inference using [birdnet-onnx-converter](https://github.com/thakala/birdnet-onnx-converter). ONNX enables deployment across a wide range of runtimes and hardware platforms including CPU, GPU, and edge devices via [ONNX Runtime](https://onnxruntime.ai/).

## Training Data

Training data combined:

- Expert-annotated clips from [Xeno-canto](https://xeno-canto.org/)
- Finnish soundscapes
- Targeted field recordings
- Selected mobile phone recordings

## Usage

The code for running inference with the model is available at: **[luomus/bird-identification](https://github.com/luomus/bird-identification)**

For building locally fine-tuned classifiers, see the BSG classifier builder: **[plauha/BSG_classifier_builder](https://github.com/plauha/BSG_classifier_builder)**

## License

Usage is limited to **non-commercial purposes**. See the [LICENSE](LICENSE) file for details.

## Citation

If you use this model, please cite:

```bibtex
@article{nokelainen2024mobile,
  title={A Mobile Application–Based Citizen Science Product to Compile Bird Observations},
  author={Nokelainen, O. and Lauha, P. and Andrejeff, S. and H{\"a}nninen, J. and Inkinen, J. and Kallio, A. and Lehto, H.J. and Mutanen, M. and Paavola, R. and Schiestl-Aalto, P. and Somervuo, P. and Sundell, J. and Talaskivi, J. and Vallinm{\"a}ki, M. and Vancraeyenest, A. and Lehti{\"o}, A. and Ovaskainen, O.},
  journal={Citizen Science: Theory and Practice},
  volume={9},
  number={1},
  pages={24},
  year={2024},
  doi={10.5334/cstp.710}
}
```

```bibtex
@article{lauha2025bsg,
  title={Bird Sounds Global - model builder: An end-to-end workflow for building locally fine-tuned bird classifiers},
  author={Lauha, Patrik and Rannisto, Meeri and Somervuo, Panu and others},
  journal={Authorea},
  year={2025},
  doi={10.22541/au.176599468.88578746/v1}
}
```

## Acknowledgments

This model was developed at the University of Jyväskylä. The ONNX conversion and optimization were performed independently to enable broader deployment options.