Mustafa5645344/insect-detection-yolov8

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)

Insect Class	Images	Instances	Precision (P)	Recall (R)	mAP@50	mAP@50-95
All Classes	2,987	3,097	0.835	0.816	0.854	0.519
stem_borer	206	223	0.957	0.978	0.992	0.794
leafhopper	208	216	0.942	0.949	0.976	0.681
ladybug	174	177	0.954	0.977	0.976	0.622
dragonfly	18	18	0.903	0.944	0.959	0.662
butterfly	186	222	0.915	0.918	0.950	0.629
weevil	58	61	0.889	0.934	0.946	0.521
rice_bug	190	237	0.931	0.916	0.937	0.595
bees	211	236	0.887	0.915	0.920	0.551
mole_cricket	39	41	0.882	0.854	0.890	0.471
grasshopper	298	317	0.882	0.852	0.885	0.510
planthopper	116	429	0.815	0.855	0.872	0.471
spider	72	78	0.767	0.821	0.858	0.572
ant	71	173	0.733	0.761	0.813	0.373
cicada	55	78	0.808	0.744	0.797	0.423
caterpillar	173	208	0.853	0.716	0.791	0.400
stink_bug	123	123	0.727	0.724	0.777	0.535
aphid	36	163	0.686	0.632	0.669	0.332
mantis	96	97	0.493	0.206	0.361	0.202

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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")