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

TensorRT8.Support Yolov5n,s,m,l,x .darknet -> tensorrt. Yolov4 Yolov3 use raw darknet *.weights and *.cfg fils. If the wrapper is useful to you,please Star it.
https://github.com/enazoe/yolo-tensorrt

darknet jetson jetson-xavier-nx l4t weights yolo-tensorrt yolov3 yolov3-tiny yolov4 yolov4-tiny yolov5 yolov5l yolov5m yolov5s yolov5x

Last synced: 6 days ago
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TensorRT8.Support Yolov5n,s,m,l,x .darknet -> tensorrt. Yolov4 Yolov3 use raw darknet *.weights and *.cfg fils. If the wrapper is useful to you,please Star it.

Host: GitHub
URL: https://github.com/enazoe/yolo-tensorrt
Owner: enazoe
License: mit
Created: 2019-12-31T06:57:43.000Z (about 5 years ago)
Default Branch: TRT8
Last Pushed: 2023-03-09T01:06:24.000Z (almost 2 years ago)
Last Synced: 2025-01-19T19:05:41.792Z (13 days ago)
Topics: darknet, jetson, jetson-xavier-nx, l4t, weights, yolo-tensorrt, yolov3, yolov3-tiny, yolov4, yolov4-tiny, yolov5, yolov5l, yolov5m, yolov5s, yolov5x
Language: C++
Homepage:
Size: 668 KB
Stars: 1,191
Watchers: 31
Forks: 315
Open Issues: 101
Metadata Files:
- Readme: README.md
- License: LICENSE

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awesome-yolo-object-detection - enazoe/yolo-tensorrt - tensorrt?style=social"/> : TensorRT8.Support Yolov5n,s,m,l,x .darknet -> tensorrt. Yolov4 Yolov3 use raw darknet *.weights and *.cfg fils. If the wrapper is useful to you,please Star it. (Lighter and Deployment Frameworks)
awesome-cuda-triton-hpc - enazoe/yolo-tensorrt - tensorrt?style=social"/> : TensorRT8.Support Yolov5n,s,m,l,x .darknet -> tensorrt. Yolov4 Yolov3 use raw darknet *.weights and *.cfg fils. If the wrapper is useful to you,please Star it. (Applications)
awesome-cuda-triton-hpc - enazoe/yolo-tensorrt - tensorrt?style=social"/> : TensorRT8.Support Yolov5n,s,m,l,x .darknet -> tensorrt. Yolov4 Yolov3 use raw darknet *.weights and *.cfg fils. If the wrapper is useful to you,please Star it. (Applications)

README

        # Yolov5 Yolov4 Yolov3 TensorRT Implementation

![GitHub stars](https://img.shields.io/github/stars/enazoe/yolo-tensorrt) ![GitHub forks](https://img.shields.io/github/forks/enazoe/yolo-tensorrt)  ![GitHub watchers](https://img.shields.io/github/watchers/enazoe/yolo-tensorrt)  [![Gitter](https://badges.gitter.im/yolo-tensorrt/community.svg)](https://gitter.im/yolo-tensorrt/community?utm_source=badge&utm_medium=badge&utm_campaign=pr-badge)

__news:__ 2021.10.31:yolov5-v6.0 support

![](./configs/result.jpg)

## INTRODUCTION

The project is the encapsulation  of nvidia official yolo-tensorrt [implementation](https://github.com/NVIDIA-AI-IOT/deepstream_reference_apps). And you must have the trained yolo model(__.weights__) and __.cfg__ file from the darknet (yolov3 & yolov4). For the [yolov5](https://github.com/ultralytics/yolov5) ,you should prepare the model file (yolov5s.yaml) and the trained weight file (yolov5s.pt) from pytorch.

![](./configs/yolo-trt.png)

- [x] yolov5n ,yolov5s , yolov5m , yolov5l , yolov5x ,yolov5-p6 [tutorial](yolov5_tutorial.md)

- [x] yolov4 

- [x] yolov3 

## 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. 

```c++

Detector detector;

Config config;

std::vector 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](https://github.com/enazoe/yolo-tensorrt/blob/master/scripts/CMakeLists.txt) perhaps could help you ._**

```bash

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 &mat_image,std::vector &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

微信关注公众号EigenVison，回复yolo获取交流群号

![](./configs/qrcode.jpeg)