---
license: cc-by-nc-4.0
pipeline_tag: image-classification
library_name: hailo
tags:
  - image-classification
  - birds
  - efficientnet
  - onnx
  - hailo
  - wildlife
datasets:
  - inaturalist
language: []
---

# BirdVision — EfficientNet-V2-S Bird Species Classifier

Fine-tuned [EfficientNet-V2-S](https://arxiv.org/abs/2104.00298) for bird species
classification across 237 North American species (Northeast / Long Island focus).

Part of the [BirdVision](https://github.com/evanwtf/birdvision) project —
real-time bird species identification from video using a Raspberry Pi 5 + Hailo-8
AI accelerator.

## Model details

| | |
|---|---|
| Base model | EfficientNet-V2-S (ImageNet-1K pretrained) |
| Input | 224×224 RGB, ImageNet normalization |
| Output | 237-class softmax logits |
| Training data | iNaturalist research-grade observations, New York state |
| Training images | ~94,800 photos across 237 species |
| Val top-1 accuracy | 80.7% |
| Val top-5 accuracy | 94.0% |

## Training

Two-phase fine-tune on an NVIDIA RTX 3080 Ti:
- **Phase 1** (5 epochs, head only): frozen backbone, LR=1e-3
- **Phase 2** (15 epochs, full): all layers unfrozen, LR=5e-5, cosine annealing

Augmentation: random resized crop, horizontal flip, rotation ±20°, color jitter.

## Usage

```python
import json
import numpy as np
import onnxruntime as ort
from PIL import Image
from huggingface_hub import hf_hub_download

# Load model and labels
onnx_path = hf_hub_download("k10z/birdvision-efficientnet-s", "efficientnet_s_birds.onnx")
labels_path = hf_hub_download("k10z/birdvision-efficientnet-s", "species_labels.json")

session = ort.InferenceSession(onnx_path, providers=["CPUExecutionProvider"])
species = json.loads(open(labels_path).read())

# Preprocess image (224×224, ImageNet normalization)
def preprocess(image_path):
    img = Image.open(image_path).convert("RGB").resize((224, 224))
    arr = np.array(img, dtype=np.float32) / 255.0
    mean = np.array([0.485, 0.456, 0.406])
    std  = np.array([0.229, 0.224, 0.225])
    arr = (arr - mean) / std
    return arr.transpose(2, 0, 1)[None]  # NCHW

# Run inference
logits = session.run(None, {"input": preprocess("bird.jpg")})[0][0]
top5 = np.argsort(logits)[::-1][:5]
for i in top5:
    print(f"{species[i]:40s} {logits[i]:.3f}")
```

## Species list

237 species — Northeast North America focus (Long Island / NY area).
See `species_labels.json` for the full list.

## Hailo-8 HEF (Raspberry Pi 5)

A compiled `efficientnet_s_birds.hef` for the [Hailo-8](https://hailo.ai/products/hailo-8/)
AI accelerator is included in this repo.

Benchmark on Raspberry Pi 5 (HailoRT 4.23.0):
- **22.3 FPS** hardware throughput
- **43.7 ms** hardware latency
- 4 contexts, 8 clusters

## License

Model weights derived from iNaturalist training data licensed
[CC BY-NC 4.0](https://creativecommons.org/licenses/by-nc/4.0/) —
**non-commercial use only**.