I build and improve computer vision systems for businesses that need to classify images, detect objects, identify defects, automate visual inspection, or integrate vision AI into an existing product.

My work goes beyond training a model. A useful computer vision product also needs reliable data preparation, inference infrastructure, confidence handling, feedback workflows, integrations, and a practical path for improving the model after deployment.

I can help with:

Image classification

Object detection

Automated quality inspection

Defect detection

Custom dataset workflows

YOLO model fine-tuning

CNN/model fine-tuning

Real-time inference pipelines

GPU-accelerated processing

Confidence and prediction workflows

Active learning

Human-review feedback loops

Model evaluation and optimization

Computer vision APIs

Integration into existing web or business systems

For one visual inspection platform, I fine-tuned YOLOv8 and ResNet models on custom datasets and helped build a real-time GPU-accelerated inference pipeline.

The resulting system achieved 97.5% detection accuracy and reduced manual inspection time by 80% for 15+ manufacturing clients.

I also implemented active-learning workflows so feedback from real users could contribute to future model improvements rather than leaving the deployed model static.

If you already have a vision system, I can begin by reviewing the dataset, model behavior, inference pipeline, and business workflow before recommending where changes will create the most value.