I will build a custom yolo object detection and tracking system
Computer Vision Engineer, YOLO, Object Detection and Tracking
Über diesen Service
Need a YOLO system that works beyond a notebook?
I build custom computer vision solutions for object detection, multi object tracking, image and video analytics, and deployment. I can help you evaluate a pretrained model, fine tune YOLO on your labeled data, build inference pipelines, integrate ByteTrack or DeepSORT, expose predictions through FastAPI, and prepare deployment for compatible cloud or NVIDIA Jetson environments.
My PyroVision fire and smoke system combined YOLO11, ONNX Runtime, FastAPI, and Next.js. It achieved 0.7642 mAP50 on a held out D Fire test set and measured 37.642 FPS on a versioned CUDA video benchmark.
You will receive:
Clear scope and technical plan
Reproducible Python source code
Model evaluation and annotated outputs
Setup instructions and documentation
Package specific API or deployment support
Before ordering, please message me with your use case, sample data, class list, target hardware, required FPS, and success criteria. Custom training requires a labeled dataset. Deployment depends on your environment and account access.
Let us define a first milestone that proves feasibility before scaling.
Mein Portfolio
FAQ
What information do you need before starting?
Please provide your use case, sample images or video, object classes, labeled dataset status, target hardware, required output, expected FPS, and success criteria.
Which package should I choose?
Choose Basic to test feasibility with a pretrained YOLO model. Choose Standard for custom model training using labeled data. Choose Premium when you also need tracking, FastAPI integration, or deployment.
Do I need to provide a labeled dataset?
Yes, custom training and fine tuning require a usable labeled dataset. Data labeling is not included unless we agree on it as a separate custom service.
Can you guarantee a specific accuracy or FPS?
Results depend on data quality, object difficulty, video conditions, and target hardware. I will help define measurable acceptance criteria and report the achieved results, but I do not promise unsupported accuracy or speed.

