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Add model card with 92.07% accuracy metrics
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metadata
license: mit
base_model: ultralytics/yolov8n-cls
tags:
  - ultralytics
  - yolo
  - vision
  - image-classification
  - pytorch
  - insects
  - pollinators
  - biodiversity
  - ecology
  - conservation
datasets:
  - custom
language:
  - en
metrics:
  - accuracy
model-index:
  - name: pollinator-classifier
    results:
      - task:
          type: image-classification
          name: Image Classification
        dataset:
          name: Custom Pollinator Insects Dataset
          type: custom
        metrics:
          - type: accuracy
            value: 0.9207
            name: Top-1 Accuracy
          - type: accuracy
            value: 0.9912
            name: Top-5 Accuracy
pipeline_tag: image-classification

Pollinator Insect Classifier 🔬

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.