Let's Quantize YOLOX from scratch with PyTorch

Model Quantization can reduce the computational and memory footprint during inference. The weights and activations are represented with low-precision data types and can operations can be performed faster. Model Quantization allows us to run models on embedded devices. The goal of this talk is to demonstrate how Model Quantization could be implemented in a Pythonic way. To do so, we're going to quantize the YOLOX object detection model completely from scratch all using PyTorch.