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)。

 在Yolov5 Yolov4 Yolov3 TensorRT 实现Implementation

 

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

  • yolov5s , yolov5m , yolov5l , yolov5x 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

 

 

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