You will get YOLOv8 Defect Detection System for Manufacturing QC
Project details
I build production-ready defect detection systems for manufacturing lines using YOLOv8 and Python. In my last industrial deployment for an automotive manufacturer, the system achieved 94.7% color detection accuracy and processed 45 inspections per hour, results that vary based on your product type, image quality, and defect complexity.
Machine Learning Tools
BERT, ChatGPT, Databricks MLflow, deeplearn.js, Google Data Studio, Google Sheets, Microsoft Excel, MLflow, NumPy, Open Neural Network Exchange, OpenCV, pandas, Python, Python Scikit-Learn, PyTorch, scikit-learn, SciPy, SQL, TensorFlow, Tesseract OCR, Vertex AI, XGBoostWhat's included $500
These options are included with the project scope.
$500
- Delivery Time 14 days
- Number of Revisions 2
- Number of Model Variations 1
- Number of Scenarios 3
- Number of Graphs/Charts 1
- Model Validation/Testing
- Model Documentation
- Data Source Connectivity
- Source Code
Frequently asked questions
About Houssem
Medical Imaging, AI Engineer, YOLOv8, MONAI, Production System
Ariana, Tunisia - 12:53 am local time
Steps for completing your project
After purchasing the project, send requirements so Houssem can start the project.
Delivery time starts when Houssem receives requirements from you.
Houssem works on your project following the steps below.
Revisions may occur after the delivery date.
Review images, set up YOLOv8 pipeline, define defect classes
Train model, evaluate accuracy, iterate to target performance