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 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 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. ONNX enables deployment across a wide range of runtimes and hardware platforms including CPU, GPU, and edge devices via ONNX Runtime.
Training Data
Training data combined:
- Expert-annotated clips from Xeno-canto
- Finnish soundscapes
- Targeted field recordings
- Selected mobile phone recordings
Usage
The code for running inference with the model is available at: luomus/bird-identification
For building locally fine-tuned classifiers, see the BSG classifier builder: plauha/BSG_classifier_builder
License
Usage is limited to non-commercial purposes. See the LICENSE file for details.
Citation
If you use this model, please cite:
@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}
}
@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.