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license: gpl-3.0
tags:
- bird-classification
- onnx
- efficientnet
- raspberry-pi
- hailo
- computer-vision
- real-time
datasets:
- duyminhle/nabirds
model-index:
- name: EfficientNet-B7 Backyard Feeder Bird Classifier
results: []
---
# 🐦 EfficientNet-B7 — Backyard Feeder Bird Classifier
Custom ONNX-based bird classification model trained on a filtered subset of the [NABirds dataset](https://dl.allaboutbirds.org/nabirds), optimized for backyard bird feeders.
> Designed to run on a Raspberry Pi + Hailo-8 setup in real-time as part of the [Birdwatcher Project](https://github.com/n2b8/birdwatcher)
---
## 🧠 Model Details
- Architecture: EfficientNet-B7
- Resolution: `600×600`
- Precision: Mixed (AMP)
- Format: ONNX
- Classes: 95 backyard species + `not_a_bird`
- Validation Accuracy: **93.14%** @ Epoch 23
- Optimized for inference on edge devices (e.g., Raspberry Pi 5 + Hailo-8)
---
## 📁 Files
- `efficientnet_b7_backyard_feeder_birds.onnx` — Trained ONNX model
- `class_labels_v3.txt` — One class label per line
---
## 🛠️ Intended Use
- **Use Case:** Real-time fine-grained classification of birds visiting backyard feeders
- **Hardware:** Optimized for Raspberry Pi + Hailo-8 AI accelerator
- **Pipeline:** Captures images via YOLOv8 detection, then classifies via this model
---
## 🐍 Example Inference Code
```python
import onnxruntime as ort
import numpy as np
from PIL import Image
# Load model
session = ort.InferenceSession("efficientnet_b7_backyard_feeder_birds.onnx")
# Preprocess
img = Image.open("bird.jpg").resize((600, 600))
x = np.array(img).astype(np.float32) / 255.0
x = np.transpose(x, (2, 0, 1))[np.newaxis, :] # CHW + batch
# Predict
outputs = session.run(None, {"input": x})
pred_idx = np.argmax(outputs[0])
```
---
## 📊 Training
- Dataset: Subset of NABirds (filtered for common backyard species)
- Augmentations: Flip, rotation, brightness
- Regularization: Dropout, label smoothing
- Loss: Cross-entropy
- Optimizer: AdamW
- Early stopping enabled
---
## 🔗 Related Repos
- [Birdwatcher Project (GitHub)](https://github.com/n2b8/birdwatcher)
- [NABirds Dataset (Kaggle)](https://www.kaggle.com/datasets/duyminhle/nabirds)
---
## 📜 License
GPLv3
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