This repository provides accelerated deployment cases of deep learning CV popular models, and cuda c supports dynamic-batch image process, infer, decode, NMS.
There are two ways to compile model(pth or onnx):

• 2023.01.01 🔥 update yolov3, yolov4, yolov5, yolov6
• 2023.01.04 🍅 update yolov7, yolox, yolor
• 2023.01.05 🎉 update u2net, libfacedetection
• 2023.01.08 🚀 The whole network is the first to support yolov8
• 2023.01.20 update efficientdet, pphunmanseg

The following environments have been tested：

# install miniconda first
conda create -n tensorrt-alpha python==3.8 -y
conda activate tensorrt-alpha
git clone https://github.com/FeiYull/tensorrt-alpha
cd tensorrt-alpha
pip install -r requirements.txt  

Installation Tutorial：

• Install For Ubuntu18.04
  
• 图文详细版：Ubuntu18.04环境配置
  
• docker for linux (waiting for update)
  
• 图文详细版：Windows10环境配置
  

set your TensorRT_ROOT path:

git clone https://github.com/FeiYull/tensorrt-alpha
cd tensorrt-alpha/cmake
vim common.cmake
# set var TensorRT_ROOT to your path in line 20, eg:
# set(TensorRT_ROOT /root/TensorRT-8.4.2.4)

start to build project: For example:yolov8

图文详细版：Windows10环境配置

At present, more than 30 models have been implemented, and some onnx files of them are organized as follows:

🍉We will test the time of all models on tesla v100 and A100! Now let's preview the performance of yolov8n on RTX2070m(8G)：

model	video resolution	model input size	GPU Memory-Usage	GPU-Util
yolov8n	1920x1080	8x3x640x640	1093MiB/7982MiB	14%

cost time per frame

yolov8n : Offical( left ) vs Ours( right )


yolov7-tiny : Offical( left ) vs Ours( right )


yolov6s : Offical( left ) vs Ours( right )


yolov5s : Offical( left ) vs Ours( right )


yolov5s : Offical( left ) vs Ours( right )


libfacedetection : Offical( left ) vs Ours( right topK:2000)

[0].https://github.com/NVIDIA/TensorRT
[1].https://github.com/onnx/onnx-tensorrt
[2].https://github.com/NVIDIA-AI-IOT/torch2trt
[3].https://github.com/shouxieai/tensorRT_Pro
[4].https://github.com/opencv/opencv_zoo