使用TensorRT实现YOLOv_C++ implementation of YOLOv6 using TensorR

# TensorRT-YOLOv6 This repo provides the C++ implementation of [YOLOv6](https://github.com/meituan/YOLOv6) based on TensorRT for performing object detection in real-time. <p align="center"> <img src="result.gif" /> </p> ## Environment - Windows 10 - Visual Studio 2017 - RTX 3070 GPU - TensorRT 7.2.1.6 - CUDA 11.1, Cudnn 8 - OpenCV 4.5.1 with CUDA ## Benchmark The following result was obtained after performing inference 1000 iterations. It includes image pre-processing, model inference and post-processing (non-max suppression) times. The resolution of the test [video](https://www.mediafire.com/file/kwxxa772ly4yy2t/test.mp4/file) was 1280x720. | Model | Device | Mode | Input Shape(HxW) | FPS | |-|-|:-:|:-:|:-:| | YOLOv6-n | RTX3070 | FP16 | 640x640 | 214 | | YOLOv6-n | RTX3070 | FP32 | 640x640 | 165 | | YOLOv6-tiny | RTX3070 | FP16 | 640x640 | 193 | | YOLOv6-tiny | RTX3070 | FP32| 640x640 | 128 | | YOLOv6-s | RTX3070 | FP16 | 640x640 | 130 | | YOLOv6-s | RTX3070 | FP32 | 640x640 | 115 | ## Tutorial - Step1: Download a pretrained onnx model from the YOLOv6 [release](https://github.com/meituan/YOLOv6/releases/tag/0.1.0) - Step2: Open the solution with Visual Studio and select `x64` and `Release` for the configuration - Step3: Go to where function `initializeSampleParams()` is defined and modify the parameters ```cpp // The engine file to generate or to load // The engine file does not exist: // This program will try to load onnx file and convert onnx into engine // The engine file exists: // This program will load the engine file directly params.engingFileName = "yolov6s.engine"; // The onnx file to load params.onnxFileName = "yolov6s.onnx"; // Number of classes (usually 80, but can be other values) params.outputClsSize = 80; // Input video filename params.inputVideoName = "test.mp4"; // Model class filename params.cocoClassNamesFileName = "coco.names"; // Threshold values params.confThreshold = 0.3; params.nmsThreshold = 0.5; // Floating point precision // By default, fp32 params.fp16 = true; ``` ## Todo - [x] Benchmark Test with different yolov6 versions - [x] Documentation - [x] Fix arguments ## Reference - [yolov4_deepstream](https://github.com/NVIDIA-AI-IOT/yolov4_deepstream/tree/master/tensorrt_yolov4) - [yolov6](https://github.com/meituan/YOLOv6)