This project utilizes Edge Impulse's FOMO algorithm, which can quickly detect objects in every frame camera captured 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. The project aims to assess Nvidia Jetson Nano's GPU capabilities in processing higher-resolution imagery (720x720) compared to typical FOMO object detection projects (often limited to 96x96), all while maintaining optimal inference speed. The machine learning model (model.eim) will be deployed using the TensorRT library, configured with GPU optimizations and integrated through the Linux C++ SDK. Additionally, the Edge Impulse model will be seamlessly integrated into our Python codebase to facilitate cumulative object counting. Our proprietary algorithm compares current frame coordinates with those of previous frames to identify new objects and avoid duplicate counting.