/yolo-tensorrt

TensorRT8.Support Yolov5s,m,l,x .darknet -> tensorrt. Yolov4 Yolov3 use raw darknet *.weights and *.cfg fils. If the wrapper is useful to you,please Star it.

Primary LanguageC++MIT LicenseMIT

Yolov5 Yolov4 Yolov3 TensorRT Implementation

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news: 2021.06.04:yolov5-v5.0 support

INTRODUCTION

The project is the encapsulation of nvidia official yolo-tensorrt implementation. And you must have the trained yolo model(.weights) and .cfg file from the darknet (yolov3 & yolov4). For the yolov5 ,you should prepare the model file (yolov5s.yaml) and the trained weight file (yolov5s.pt) from pytorch.

  • yolov5s , yolov5m , yolov5l , yolov5x ,yolov5-p6 tutorial
  • yolov4 , yolov4-tiny
  • yolov3 , yolov3-tiny

Features

  • inequal net width and height
  • batch inference
  • support FP32,FP16,INT8
  • dynamic input size

PLATFORM & BENCHMARK

  • windows 10
  • ubuntu 18.04
  • L4T (Jetson platform)
BENCHMARK

x86 (inference time)

model size gpu fp32 fp16 INT8
yolov5s 640x640 1080ti 8ms / 7ms
yolov5m 640x640 1080ti 13ms / 11ms
yolov5l 640x640 1080ti 20ms / 15ms
yolov5x 640x640 1080ti 30ms / 23ms

Jetson NX with Jetpack4.4.1 (inference / detect time)

model size gpu fp32 fp16 INT8
yolov3 416x416 nx 105ms/120ms 30ms/48ms 20ms/35ms
yolov3-tiny 416x416 nx 14ms/23ms 8ms/15ms 12ms/19ms
yolov4-tiny 416x416 nx 13ms/23ms 7ms/16ms 7ms/15ms
yolov4 416x416 nx 111ms/125ms 55ms/65ms 47ms/57ms
yolov5s 416x416 nx 47ms/88ms 33ms/74ms 28ms/64ms
yolov5m 416x416 nx 110ms/145ms 63ms/101ms 49ms/91ms
yolov5l 416x416 nx 205ms/242ms 95ms/123ms 76ms/118ms
yolov5x 416x416 nx 351ms/405ms 151ms/183ms 114ms/149ms

ubuntu

model size gpu fp32 fp16 INT8
yolov4 416x416 titanv 11ms/17ms 8ms/15ms 7ms/14ms
yolov5s 416x416 titanv 7ms/22ms 5ms/20ms 5ms/18ms
yolov5m 416x416 titanv 9ms/23ms 8ms/22ms 7ms/21ms
yolov5l 416x416 titanv 17ms/28ms 11ms/23ms 11ms/24ms
yolov5x 416x416 titanv 25ms/40ms 15ms/27ms 15ms/27ms

WRAPPER

Prepare the pretrained .weights and .cfg model.

Detector detector;
Config config;

std::vector<BatchResult> res;
detector.detect(vec_image, res)

Build and use yolo-trt as DLL or SO libraries

windows10

  • dependency : TensorRT 7.1.3.4 , cuda 11.0 , cudnn 8.0 , opencv4 , vs2015

  • build:

    open MSVC sln/sln.sln file

    • dll project : the trt yolo detector dll
    • demo project : test of the dll

ubuntu & 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

struct 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::vector<cv::Mat> &mat_image,std::vector<BatchResult> &vec_batch_result);

private:
	Detector(const Detector &);
	const Detector &operator =(const Detector &);
	class Impl;
	Impl *_impl;
};

REFERENCE

Contact

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