在Yolov5 Yolov4 Yolov3 TensorRT 实现Implementation

在Yolov5 Yolov4 Yolov3 TensorRT实现Implementation

news: yolov5 support

引论

该项目是nvidia官方yolo-tensorrt的封装实现。你必须有经过训练的yolo模型（.weights）和来自darknet（yolov3&yolov4）的.cfg文件。对于yolov5，需要Pythorch中的模型文件（yolov5s.yaml）和经过训练的权重文件（yolov5s.pt）。

参考：https://github.com/enazoe/yolo-tensorrt

BENCHMARKx86 (inference time)modelsizegpufp32fp16INT8yolov5s640x6401080ti8ms/7msyolov5m640x6401080ti13ms/11msyolov5l640x6401080ti20ms/15msyolov5x640x6401080ti30ms/23msJetson NX with Jetpack4.4.1 (inference / detect time)modelsizegpufp32fp16INT8yolov3416x416nx105ms/120ms30ms/48ms20ms/35msyolov3-tiny416x416nx14ms/23ms8ms/15ms12ms/19msyolov4-tiny416x416nx13ms/23ms7ms/16ms7ms/15msyolov4416x416nx111ms/125ms55ms/65ms47ms/57msyolov5s416x416nx47ms/88ms33ms/74ms28ms/64msyolov5m416x416nx110ms/145ms63ms/101ms49ms/91msyolov5l416x416nx205ms/242ms95ms/123ms76ms/118msyolov5x416x416nx351ms/405ms151ms/183ms114ms/149msubuntumodelsizegpufp32fp16INT8yolov4416x416titanv11ms/17ms8ms/15ms7ms/14msyolov5s416x416titanv7ms/22ms5ms/20ms5ms/18msyolov5m416x416titanv9ms/23ms8ms/22ms7ms/21msyolov5l416x416titanv17ms/28ms11ms/23ms11ms/24msyolov5x416x416titanv25ms/40ms15ms/27ms15ms/27msWRAPPERPrepare the pretrained .weights and .cfg model.Detector detector;
Config config;

std::vector res;
detector.detect(vec_image, res)Build and use yolo-trt as DLL or SO librarieswindows10dependency : TensorRT 7.1.3.4 , cuda 11.0 , cudnn 8.0 , opencv4 , vs2015build:open MSVC sln/sln.sln filedll project : the trt yolo detector dlldemo project : test of the dllubuntu & L4T (jetson)The project generate the libdetector.so lib, and the sample code. If you want to use the libdetector.so lib in your own project,this cmake file perhaps could help you .git clone https://github.com/enazoe/yolo-tensorrt.git
cd yolo-tensorrt/
mkdir build
cd build/
cmake …
make
./yolo-trt
API
config{ std::string file_model_cfg = “configs/yolov4.cfg”; std::string file_model_weights = “configs/yolov4.weights”; float detect_thresh = 0.9; ModelType net_type = YOLOV4; Precision inference_precison = INT8; int gpu_id = 0; std::string calibration_image_list_file_txt = “configs/calibration_images.txt”; }; class API Detector{public: explicit Detector(); ~Detector(); void init(const Config &config); void detect(const std::vectorcv::Mat &mat_image,std::vector &vec_batch_result); private: Detector(const Detector &); const Detector &operator =(const Detector &); class Impl; Impl *_impl;};

REFERENCE

https://github.com/wang-xinyu/tensorrtx/tree/master/yolov4
https://github.com/mj8ac/trt-yolo-app_win64
https://github.com/NVIDIA-AI-IOT/deepstream_reference_apps

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