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martinbadrous/
Microparticle-Detection-YOLOv5

Name: Microparticle-Detection-YOLOv5
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🔬 YOLOv5 — Microparticle Detection & Classification

Automated detection and classification of microplastics from microscopy images. Developed for industrial deployment at IZONICS — achieving >90% accuracy in production.

📌 Model Summary

Property	Details
🏗️ Architecture	YOLOv5
🎯 Task	Object Detection + Classification
📊 Accuracy	>90% in production
⚡ Speedup	30% faster than classical image processing pipeline
🏭 Deployment	Fully industrialized — prototype to production
⚙️ Framework	PyTorch
📜 License	MIT

🧠 What This Model Does

This model detects and classifies microplastic particles in microscopy images, enabling automated environmental monitoring and industrial quality control.

It was developed at IZONICS as a complete refactoring of an existing classical computer vision pipeline into a deep learning architecture — resulting in higher reliability, better accuracy, and significantly faster inference in real production environments.

Origin: Extended from a Master's thesis (VIBOT program, Université de Bourgogne) into a fully deployed industrial product.

📈 Performance

Metric	Score
Production accuracy	>90%
Processing speedup vs. classical pipeline	30%
Deployment status	✅ Fully industrialized

🚀 How to Use

python

import torch

# Load the model
model = torch.hub.load("ultralytics/yolov5", "custom", path="best.pt")
model.conf = 0.35  # confidence threshold

# Run inference
results = model("microscopy_image.jpg")

# Show results
results.show()

# Get detections as dataframe
df = results.pandas().xyxy[0]
print(df)

🗂️ Training Data

Domain: Environmental microscopy images of microplastic particles
Task: Multi-class detection and classification of particle types
Augmentation: Custom augmentation tailored for microscopy image characteristics

⚠️ Limitations

Optimized for specific microscopy imaging conditions used at IZONICS
Performance may vary on microscopy setups with different magnification or lighting
Not validated for clinical or regulatory use