I will optimize and deploy computer vision yolo models on raspberry pi and edge devices

Are you struggling to run your heavy AI models on a Raspberry Pi?

Many developers can build a Computer Vision model in a Python notebook, but getting it to run smoothly on constrained edge hardware is a completely different challenge.

I am an AI/ML Software Engineer specializing in Edge AI. I will optimize, accelerate, and deploy your complex vision models to run seamlessly on devices like Raspberry Pi, and Intel NUCs.

My Expertise & Proven Results:

I recently engineered and deployed real-time vision pipelines. Through advanced optimization (ONNX, OpenVINO, NCNN), I successfully run heavy models on edge hardware achieving >98% accuracy while reducing processing latency down to an incredible 20-50ms.

What I can do for you:

• Convert PyTorch/TensorFlow models to highly optimized Edge formats (ONNX, TFLite, OpenVINO).

• Deploy YOLO object detection, face recognition, or human tracking models directly onto your hardware.

• Build a robust FastAPI backend to handle real-time video streaming from your device.

• Hardware I work with: Raspberry Pi, Intel NUC, Edge TPUs.

Please message me before placing an order to discuss your exact hardware and project requirements!