About the service
I create computer vision systems that turn camera streams and video into solutions your product or production line can rely on. My main areas are object detection and tracking, image and scene classification, text recognition (OCR) on documents and complex industrial surfaces, and automated defect control in manufacturing. I lead the full project: from a quick prototype showing achievable accuracy, through data collection and labeling, to a trained model running stably in production. At each stage you work with the person actually writing code and tuning the model.
I start with your data, not a trendy architecture. I assess what images you have, design a labeling scheme around how you make decisions, and pick a model family based on your latency and hardware budget, not benchmark tables. I train custom detectors and classifiers, handle the unpleasant parts — class imbalance, varying lighting, motion blur, rare defect types — and validate everything on a held-out set reflecting real operating conditions. When accuracy hits a ceiling, I know where to look: good data often outweighs a bigger network, and I'll tell you honestly if that's the bottleneck.
Deployment is the stage where many vision projects quietly fail, so I treat it as a full deliverable. I package models behind a clean inference-API or optimize and quantize them to run on edge devices and embedded boards right next to the camera. You get monitoring, a mechanism to collect problematic frames for retraining and documentation for the next engineer. Whether you need a one-time feasibility check or a production system for years ahead — I clearly scope the work and deliver something that actually works.
Typical tasks:
— Detection and multi-object tracking for video and live streams
— OCR pipelines for invoices, labels, counters and engraving
— Automated visual quality control on the production line
— Training custom models and deployment via API or edge
I start with your data, not a trendy architecture. I assess what images you have, design a labeling scheme around how you make decisions, and pick a model family based on your latency and hardware budget, not benchmark tables. I train custom detectors and classifiers, handle the unpleasant parts — class imbalance, varying lighting, motion blur, rare defect types — and validate everything on a held-out set reflecting real operating conditions. When accuracy hits a ceiling, I know where to look: good data often outweighs a bigger network, and I'll tell you honestly if that's the bottleneck.
Deployment is the stage where many vision projects quietly fail, so I treat it as a full deliverable. I package models behind a clean inference-API or optimize and quantize them to run on edge devices and embedded boards right next to the camera. You get monitoring, a mechanism to collect problematic frames for retraining and documentation for the next engineer. Whether you need a one-time feasibility check or a production system for years ahead — I clearly scope the work and deliver something that actually works.
Typical tasks:
— Detection and multi-object tracking for video and live streams
— OCR pipelines for invoices, labels, counters and engraving
— Automated visual quality control on the production line
— Training custom models and deployment via API or edge
