DeepStream-Yolo-master

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DeepStream-Yolo-master

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deepstream使用yolov5的详细教程与代码文件

DeepStream-Yolo-master 최신버젼 다운로드

최종 버전 다운로드 (.zip)

// repository documentation

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# YOLOv5 usage in deepstream6.2

# 一.环境安装
## yolov5环境安装
参考官网:https://github.com/ultralytics/yolov5/tree/v6.0
## deepstream环境安装
参考官网:https://docs.nvidia.com/metropolis/deepstream/6.0.1/dev-guide/text/DS_Quickstart.html

# 二.wts与cfg获得
## 获得wts与cfg
将gen_wts_yoloV5.py复制到yolov5-6.0中，运行此文件，如下图所示：

![img_3.png](https://raw.githubusercontent.com/tangjunjun966/DeepStream-Yolo-master/master/imgs/img_3.png)

## 修改wts
通过查看wts有多少行，获得模型层，并在wts文件头行添加数字，如下图所示：

![img_4.png](https://raw.githubusercontent.com/tangjunjun966/DeepStream-Yolo-master/master/imgs/img_4.png)

修改如下：

![img_5.png](https://raw.githubusercontent.com/tangjunjun966/DeepStream-Yolo-master/master/imgs/img_5.png)

说明：n与s是291，m是401
# 三.libnvdsinfer_custom_impl_Yolo.so库生成
在DeepStream-Yolo路径下执行以下命令：
```c
CUDA_VER=11.4 make -C nvdsinfer_custom_impl_Yolo
```
出现如下编译:

![img_8.png](https://raw.githubusercontent.com/tangjunjun966/DeepStream-Yolo-master/master/imgs/img_8.png)

编译成功后会出现libnvdsinfer_custom_impl_Yolo.so，如下图：

![img_9.png](https://raw.githubusercontent.com/tangjunjun966/DeepStream-Yolo-master/master/imgs/img_9.png)

# 四.修改配置文件
主要修改文件路径，如下图所示：

![img_10.png](https://raw.githubusercontent.com/tangjunjun966/DeepStream-Yolo-master/master/imgs/img_10.png)

# 五.运行demo

```c
$cd  /opt/nvidia/deepstream/deepstream-6.2/sources/DeepStream-Yolo
$deepstream-app -c deepstream_app_config.txt
```
初次加载模型，需要构建engine转换，时间较长,加载完后会产生engine文件，不删除，下次运行无需编译可快速运行。
加载好模型会出现*.engine文件,如下所示：

![img_13.png](https://raw.githubusercontent.com/tangjunjun966/DeepStream-Yolo-master/master/imgs/img_13.png)

运行成功会出现如下所示:

![img_14.png](https://raw.githubusercontent.com/tangjunjun966/DeepStream-Yolo-master/master/imgs/img_14.png)

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