This project uses Edge Impulse’s FOMO (Faster Objects, More Objects) which can quickly detect objects and use them as a quality/quantity check for products on a running conveyor belt. FOMO's ability to know the number and position of coordinates of an object is the basis of this system. This project will explore the capability of NVIDIA Jetson Nano’s GPU to handle color video (RGB) with a higher resolution (320x320) while still maintaining high inference speed. The ML model (model.eim) will be deployed with the TensorRT library which will be compiled with optimizations for the GPU which will be setup via the Linux C++ SDK. Once the model can identify different pizza toppings, an additional Python program will be added, so it’s able to check each pizzas to see whether it meets the standard quantity of pepperonis, mushrooms, and paprikas. This project is a proof-of-concept that can be widely applied in the product manufacturing and food production industries to perform quality check based on the quantity requirements of objects in each product.