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README.md

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+---
+license: mit
+tags:
+  - bioacoustics
+  - few-shot-learning
+  - prototypical-networks
+  - bird-identification
+  - individual-identification
+  - audio
+  - mel-spectrogram
+datasets:
+  - custom
+language:
+  - en
+pipeline_tag: audio-classification
+---
+
+# MUNIN: Metric-learning Unit for Non-invasive Individual Naming
+
+Lightweight few-shot learning for acoustic individual identification in birds. A ResNet18 encoder trained from scratch on mel spectrograms using episodic prototypical learning.
+
+## Key Result
+
+MUNIN (11M parameters, 512-d embeddings) achieves parity with BirdNET (pretrained on 6,000+ species, 1024-d) for individual bird identification:
+
+| Setting | MUNIN | BirdNET | Perch |
+|---------|-------|---------|-------|
+| 1-shot  | **85.0%** | 81.4% | 80.4% |
+| 3-shot  | 89.9% | **91.5%** | 90.8% |
+| 5-shot  | 93.9% | **94.1%** | 92.8% |
+
+TOST equivalence at 5-shot within +/-2pp margin (p=0.0013). Evaluated on 9 held-out individuals across 3 species.
+
+## Checkpoints
+
+| File | Description |
+|------|-------------|
+|  | **MUNIN flagship** -- best 5-shot model (93.9%) |
+|  | 3-shot variant (89.9%) |
+|  | 1-shot variant (85.0%, leads BirdNET) |
+
+## Usage
+
+
+
+## Input Format
+
+- Mono audio at 22050 Hz
+- Mel spectrogram: 128 bins, 1.5s clips (65 frames)
+- Shape: 
+
+## Training
+
+- Episodic prototypical learning (5-way 5-shot)
+- 27 training individuals across 3 species (cockatoo, penguin, little owl)
+- 50 epochs, 200 episodes/epoch, cosine annealing LR
+- Trained on consumer GPU (RTX 4070, ~15 min)
+
+## Paper
+
+Preprint: [MUNIN: Lightweight few-shot learning achieves parity with large pretrained encoders for acoustic individual identification in birds](https://github.com/j8ckfi/MUNIN)
+
+## Citation
+
+