enazoe / Yolo Tensorrt
Licence: mit
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.
Stars: ✭ 495
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Yolov5 Yolov4 Yolov3 TensorRT Implementation
news: yolov5-v4.0 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.
- [x] yolov5s , yolov5m , yolov5l , yolov5x tutorial
- [x] yolov4 , yolov4-tiny
- [x] yolov3 , yolov3-tiny
Features
- [x] inequal net width and height
- [x] batch inference
- [x] support FP32,FP16,INT8
- [ ] dynamic input size
PLATFORM & BENCHMARK
- [x] windows 10
- [x] ubuntu 18.04
- [x] 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
- 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
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