Birdvision Efficientnet S

--- 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. ##...

Source context: 9 downloads · 1 likes · Pipeline image-classification · Library hailo · Repo k10z/birdvision-efficientnet-s