Coop Cam Bird Classifier
Int8 quantized MobileNetV2 for on-device bird species classification on ESP32-S3.
Model Details
- Architecture: MobileNetV2 (width_mult=0.25)
- Input: 96x96 grayscale (int8), shape [1, 96, 96, 1]
- Output: 168 North American bird species (int8)
- Size: 612 KB (TFLite int8)
- Validation accuracy: 58.8% (post-quantization)
Training
Knowledge distillation from chriamue/bird-species-classifier (EfficientNet-B2, 525 classes) using the chriamue/bird-species-dataset.
- Distillation temperature: 4.0
- Alpha (KL weight): 0.7
- Optimizer: AdamW (lr=1e-3, weight_decay=1e-4)
- Scheduler: Cosine annealing over 50 epochs
- Early stopping: Patience 10
Files
| File | Description |
|---|---|
| Int8 quantized TFLite model (deploy to ESP32) | |
| C array header for embedding in firmware | |
| C header with species name array | |
| PyTorch checkpoint (for retraining) | |
| ONNX intermediate (for re-export) |
Usage
For ESP32-S3 firmware, include and in your build.
To regenerate from the TFLite file:
License
MIT (derived from MIT-licensed teacher model and CC0 dataset)
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