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:
from huggingface_hub import hf_hub_download from ultralytics import YOLO # pick the weights file from this repo's "Files and versions" tab weights = hf_hub_download("leonelgv/pollinator-classifier", "<weights>.pt") model = YOLO(weights) source = 'http://images.cocodataset.org/val2017/000000039769.jpg' model.predict(source=source, save=True) - Notebooks
- Google Colab
- Kaggle
Pollinator Insect Classifier π¬
High-precision classifier for 10 pollinator insect species using YOLOv8 Nano with 92.07% accuracy.
Quick Start
from ultralytics import YOLO
from huggingface_hub import hf_hub_download
# Download model
model_path = hf_hub_download("leonelgv/pollinator-classifier", "yolo8n.pt")
# Load and predict
model = YOLO(model_path)
results = model("insect_image.jpg")
See files in this repository for complete usage examples and training details.
- Downloads last month
- 26
Evaluation results
- Top-1 Accuracy on Custom Pollinator Insects Datasetself-reported0.921
- Top-5 Accuracy on Custom Pollinator Insects Datasetself-reported0.991