Part 4: Deploying Detectron2 Models into Production
This last part walks you through the steps in an export process to convert Detectron2 models into deployable artifacts. Specifically, it describes the standard file formats of deep learning models such as TorchScript and corresponding runtimes for these formats, such as PyTorch and C++ runtimes. It then provides the steps to convert Detectron2 models to the standard file formats and deploy them to the corresponding runtimes. Additionally, this part introduces Open Neural Network Exchange (ONNX) framework. This framework helps share deep neural networks across multiple frameworks and platforms. It is extremely helpful when deploying Detectron2 models into browsers or mobile environments is needed. Finally, this part describes D2Go, a framework for training, quantizing, and deploying models with minimal memory storage and computation requirements. Models created using this framework are extremely helpful for deploying into mobile or...