If you haven’t yet, make sure you carefully read last week’s tutorial on configuring and installing OpenCV with NVIDIA GPU support for the “dnn” module - following that tutorial is an absolute prerequisite for this tutorial. Compile OpenCV’s ‘dnn’ module with NVIDIA GPU support Figure 1: Compiling OpenCV’s DNN module with the CUDA backend allows us to perform object detection with YOLO, SSD, and Mask R-CNN deep learning models much faster.
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Inside this tutorial you’ll learn how to implement Single Shot Detectors, YOLO, and Mask R-CNN using OpenCV’s “deep neural network” ( dnn) module and an NVIDIA/CUDA-enabled GPU. Looking for the source code to this post? Jump Right To The Downloads Section OpenCV ‘dnn’ with NVIDIA GPUs: 1,549% faster YOLO, SSD, and Mask R-CNN To learn how to use OpenCV’s dnn module and an NVIDIA GPU for faster object detection and instance segmentation, just keep reading! Mask R-CNN instance segmentation at 11.05 FPS.Single Shot Detectors (SSDs) at 65.90 FPS.Today we’re going to discuss complete code examples in more detail - and by the end of the tutorial, you’ll be able to apply: Using OpenCV’s GPU-optimized dnn module we were able to push a given network’s computation from the CPU to the GPU in only three lines of code: # load the model from disk and set the backend target to a Last week, we discovered how to configure and install OpenCV and its “deep neural network” ( dnn) module for inference using an NVIDIA GPU.
In this tutorial, you’ll learn how to use OpenCV’s “dnn” module with an NVIDIA GPU for up to 1,549% faster object detection (YOLO and SSD) and instance segmentation (Mask R-CNN).
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