Overview
We build computer vision systems for environments where off-the-shelf models fail — low-light, domain-specific classes, real-time edge requirements, and classified use cases. Our work spans defence perimeter monitoring, sports performance analytics, and industrial inspection.
The Problem
Pretrained COCO models do not understand your domain. A model trained on public datasets misidentifies objects in low-light, thermal, or domain-specific contexts. Real-time edge deployment adds latency and hardware constraints that most cloud-trained models cannot meet.
Our Approach
We fine-tune YOLOv8x on client-provided datasets, build custom data annotation pipelines, and optimise for edge hardware using ONNX or TensorRT. For classified environments, all training and inference is on-premise. We maintain evaluation benchmarks and regression test against precision/recall targets on every model update.
Deliverables
- Custom model fine-tuning (YOLOv8x)
- Data annotation pipeline
- Edge hardware optimisation
- Real-time inference API
- Model evaluation benchmarks
- On-premise deployment