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README.md

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 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_fp32.onnx` | Full precision (FP32) | GPU (CUDA/TensorRT), Desktop CPU |
-| `BSG_birds_Finland_v4_4_fp16.onnx` | Half precision (FP16) | RPi 5, Modern GPUs |
+| `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