Yolov5 Yolov4 Yolov3 TensorRT Implementation
news: yolov5 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 tutorial
- yolov4 , yolov4-tiny
- yolov3 , yolov3-tiny
Features
- inequal net width and height
- batch inference
- support FP32,FP16,INT8
- daynamic input size
PLATFORM & PERFORMENCE
- windows 10
- ubuntu 18.04
- L4T (Jetson platform)
model | gpu | precision | detect time(with pre and post process) |
---|---|---|---|
yolov3-416x416 | jetson nano (15w) | FP16 | 250ms |
yolov3-416x416 | jetson xavier nx (15w 6core) | FP32 | 120ms |
yolov3-416x416 | jetson xavier nx (15w 6core) | FP16 | 45ms |
yolov3-416x416 | jetson xavier nx (15w 6core) | INT8 | 35ms |
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
C++
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;
};
C# using C++ dll
private const string YoloLibraryName = "detector.dll";
[DllImport(YoloLibraryName, EntryPoint = "init")]
private static extern int InitializeYolo(string configurationFilename, string weightsFilename);
[DllImport(YoloLibraryName, EntryPoint = "detect_image")]
private static extern int DetectImage(string filename, ref BboxContainer container);
[DllImport(YoloLibraryName, EntryPoint = "detect_mat")]
private static extern int DetectImage(IntPtr pArray, int nSize, ref BboxContainer container);
[DllImport(YoloLibraryName, EntryPoint = "dispose")]
private static extern int DisposeYolo();
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
Contact
qq group id: 1151955802