Hello AI World guide to deploying deep-learning inference networks and deep vision primitives with TensorRT and NVIDIA Jetson.
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Updated
Apr 19, 2024 - C++
Hello AI World guide to deploying deep-learning inference networks and deep vision primitives with TensorRT and NVIDIA Jetson.
⚡️An Easy-to-use and Fast Deep Learning Model Deployment Toolkit for ☁️Cloud 📱Mobile and 📹Edge. Including Image, Video, Text and Audio 20+ main stream scenarios and 150+ SOTA models with end-to-end optimization, multi-platform and multi-framework support.
TensorRT8.Support Yolov5n,s,m,l,x .darknet -> tensorrt. Yolov4 Yolov3 use raw darknet *.weights and *.cfg fils. If the wrapper is useful to you,please Star it.
Perception and AI components for autonomous mobile robotics.
Robotics with GPU computing
Visual odometry package based on hardware-accelerated NVIDIA Elbrus library with world class quality and performance.
Real-time C++ ECO tracker etc. speed-up by SSE/NEON, support Linux, Mac, Jetson TX1/2, raspberry pi
World's fastest ANPR / ALPR implementation for CPUs, GPUs, VPUs and NPUs using deep learning (Tensorflow, Tensorflow lite, TensorRT, OpenVX, OpenVINO). Multi-Charset (Latin, Korean, Chinese) & Multi-OS (Jetson, Android, Raspberry Pi, Linux, Windows) & Multi-Arch (ARM, x86).
Hardware-accelerated 3D scene reconstruction and Nav2 local costmap provider using nvblox
Image processing software on GPU (Windows, Linux, ARM) for real time machine vision camera applications. Performance benchmarks and Glass-to-Glass time measurements. MIPI CSI cameras support. RAW2RGB processing on CUDA with 16-bit ISP. Software for Jetson.
This is a DeepStream application to demonstrate a human pose estimation pipeline.
A C++ library that enables the use of Jetson's GPIOs
A shared library of on-demand DeepStream Pipeline Services for Python and C/C++
Sample projects for TensorRT in C++
Hardware-accelerated ROS2 packages for camera image processing.
Hardware-accelerated DNN model inference ROS 2 packages using NVIDIA Triton/TensorRT for both Jetson and x86_64 with CUDA-capable GPU
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