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.
| model | gpu | precision | memory used | inference time |
|---|---|---|---|---|
| yolov3-416x416 | gtx1050 | INT8 | 25ms | |
| yolov3-416x416 | gtx1050 | FLOAT32 | 50ms | |
| yolov3-608x608 | gtx1050 | INT8 | ~450M | ~50ms |
| yolov3-608x608 | gtx1050 | FLOAT32 | ~1000M | ~95ms |
| yolov3-416x416 | jetson nano (15w) | HALF(FP16) | 250ms |
Prepare the pretrained .weights and .cfg model.
Detector detector;
Config config;
config.inference_precison = INT8;
detector.init(config);
cv::Mat mat_image = cv::imread("dog.jpg");
std::vector<Result> res;
detector.detect(mat_image, res)-
dependency : cuda 10.0 , cudnn 7.5 , TensorRT 5.1.5 , opencv3.3 , gflags , vs2015
-
build:
open MSVC sln/sln.sln file
- dll project : the interface of the trt yolo dll
- demo project : test of the dll
dependency : gflags , JetPack 4.2.2
sudo apt-get install libgflags-dev
cd yolo-tensorrt/
mkdir build
cd build/
cmake ..
make
The project generate the libdetector.so lib, and the sample code. If you want to use the generated libdetector.so lib in your own project,the cmake file perhaps could help you in scripts dir.
note: when the platform is jetson nano the gencode arch must be set compute_53,code=sm_53 at cmake file.
struct Config
{
std::string file_model_cfg = "configs/yolov3.cfg";
std::string file_model_weights = "configs/yolov3.weights";
float detect_thresh = 0.9;
ModelType net_type = YOLOV3;
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 cv::Mat &mat_image, std::vector<Result> &vec_result);
private:
Detector(const Detector &);
const Detector &operator =(const Detector &);
class Impl;
Impl *_impl;
};