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
File size: 1,333 Bytes
77be4ef | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 | #!/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()
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