wildflower-classifier-vit

🌸 Wildflower Classifier — ViT Base

A Vision Transformer (ViT) fine-tuned to identify 25 North American wildflower species from photographs, achieving 97.9% test accuracy.

Model Details

• Base model: google/vit-base-patch16-224
• Architecture: ViT-Base (86.6M params, patch size 16, input 224×224)
• Task: Multi-class image classification (25 classes)
• Training data: meganariley/inat-wildflowers-25 — 10,000 research-grade iNaturalist photos

Performance

Per-Class Test Accuracy

10 species at 100% accuracy · All species ≥ 95%

Training

• Epochs: 5
• Optimizer: AdamW (lr=5e-5, weight_decay=0.01)
• Batch size: 16 × 4 gradient accumulation = effective 64
• Warmup: 10% linear
• Augmentation: RandomResizedCrop, HorizontalFlip, ColorJitter
• Hardware: CPU (8 vCPU / 32GB RAM)
• Training time: ~2 hours

Training Curve

Usage

from transformers import pipeline

classifier = pipeline("image-classification", model="meganariley/wildflower-classifier-vit")
results = classifier("path/to/wildflower.jpg", top_k=5)
for r in results:
    print(f"{r['label']}: {r['score']:.3f}")

Demo

Try it live: 🌸 Wildflower Identifier Space

License

Apache 2.0 (base model license). Training data from iNaturalist under CC BY-NC 4.0.