TensorRT for Beginners: A Tutorial on Deep Learning Inference Optimization

In this tutorial we are exploring NVIDIA's TensorRT, a deep learning inference optimizer. We are walking step-by-step through a Jupyter notebook that boosts inference speeds for an object detection model using pyTorch. This tutorial is intended to be beginner-friendly but requires prior knowledge in Python and familiarity with Neural Networks and Machine Learning. Jupyter notebook: https://github.com/CactusQ/tensor_rt_for_beginners Further Reading: https://developer.nvidia.com/tensorrt https://medium.com/@abhaychaturvedi_72055/understanding-nvidias-tensorrt-for-deep-learning-model-optimization-dad3eb6b26d9 https://github.com/ultralytics/yolov5 https://cocodataset.org/ Edit: In the video I said TensorScript but meant TorchScript. At the very end I also mention "MAP" without explaining Mean Average precision: https://kili-technology.com/data-labeling/machine-learning/mean-average-precision-map-a-complete-guide