Image Classification
ultralytics
PyTorch
English
yolo
vision
insects
pollinators
biodiversity
ecology
conservation
Eval Results (legacy)
Instructions to use leonelgv/pollinator-classifier with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- ultralytics
How to use leonelgv/pollinator-classifier with ultralytics:
# Couldn't find a valid YOLO version tag. # Replace XX with the correct version. from ultralytics import YOLOvXX model = YOLOvXX.from_pretrained("leonelgv/pollinator-classifier") source = 'http://images.cocodataset.org/val2017/000000039769.jpg' model.predict(source=source, save=True) - Notebooks
- Google Colab
- Kaggle
| #!/usr/bin/env python3 | |
| """ | |
| Script de prueba rápida para el clasificador | |
| """ | |
| from pollinator_classifier import PollinatorClassifier | |
| import os | |
| def test_classifier(): | |
| """Probar el clasificador con imágenes del dataset""" | |
| print("🧪 PRUEBA RÁPIDA DEL CLASIFICADOR") | |
| print("=" * 50) | |
| # Inicializar clasificador | |
| try: | |
| classifier = PollinatorClassifier() | |
| print("✅ Clasificador inicializado") | |
| except Exception as e: | |
| print(f"❌ Error: {e}") | |
| return | |
| # Buscar imágenes de prueba | |
| test_folder = "/home/leonel/sistema_polinizador/Dataset/Classification_YOLO/test" | |
| if os.path.exists(test_folder): | |
| print(f"📁 Buscando imágenes en: {test_folder}") | |
| # Probar con una imagen de cada clase | |
| from pathlib import Path | |
| test_path = Path(test_folder) | |
| for class_dir in list(test_path.iterdir())[:3]: # Solo 3 clases para no saturar | |
| if class_dir.is_dir(): | |
| images = list(class_dir.glob("*.jpg")) + list(class_dir.glob("*.png")) | |
| if images: | |
| print(f"\n🔍 Probando clase: {class_dir.name}") | |
| classifier.classify(str(images[0])) | |
| else: | |
| print(f"❌ No se encontró carpeta de test: {test_folder}") | |
| if __name__ == "__main__": | |
| test_classifier() | |