Insect Detection: YOLOv8m Object Detection Model

This repository provides production-ready YOLOv8m (Medium) weights fine-tuned on 50,000 augmented agricultural images (specifly-3-rwm7i) for real-time localization and detection of insect pests in fields and greenhouses.

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Performance Highlights & Overall Metrics

• mAP@50: 85.72% (0.8572 peak, 0.8540 validation)
• mAP@50-95: 51.87%
• Precision: 83.46%
• Recall: 85.86%
• F1-Score: 82.40%
• Inference Speed: 1.5 ms per image (~400+ FPS on NVIDIA A100)
• Model Parameters: 23.23M | GFLOPs: 67.9

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Detailed Per-Class Validation Breakdown (mAP@50)

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Training Setup

• Framework: Ultralytics YOLOv8 (v8.3.0)
• Hardware: NVIDIA A100-SXM4-40GB
• Epochs: 50
• Batch Size: 56 | Image Size: 640 x 640
• Optimizer: AdamW (lr0 = 0.0008, lrf = 0.00008 with Cosine Annealing)
• Dataset Source: Roboflow specifly-3-rwm7i (Augmented Version)

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Quick Usage

from ultralytics import YOLO
from huggingface_hub import hf_hub_download

# Download best weights
weights_path = hf_hub_download(
    repo_id="Mustafa5645344/insect-detection-yolov8",
    filename="best.pt"
)

# Run Inference
model = YOLO(weights_path)
results = model.predict(source="field_sample.jpg", conf=0.35, save=True)

for r in results:
    for box in r.boxes:
        cls_id = int(box.cls[0].item())
        conf = float(box.conf[0].item())
        print(f"Found: {model.names[cls_id]} with {conf*100:.1f}% confidence")