yolov5-lite
时间: 2023-10-21 11:33:01 浏览: 40
Yolov5-lite is a lightweight version of the popular object detection model YOLOv5. It is designed to be faster and more efficient than the full version of YOLOv5, while still maintaining high accuracy in object detection tasks. Yolov5-lite is optimized for deployment on mobile devices or embedded systems with limited computing resources. It uses a smaller model architecture and fewer parameters than the full version of YOLOv5, making it a good choice for real-time object detection applications on resource-limited devices.
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yolov5-lite模型转换
yolov5-lite模型转换的过程可以分为以下几个步骤。首先,将模型转换为ONNX格式,可以使用命令"./onnx2ncnn"来进行转换。例如,"./onnx2ncnn yolov5ss-sim.onnx yolov5-lite.param yolov5-lite.bin"。\[1\]接下来,可以使用命令"./ncnnoptimize"对转换后的模型进行优化,例如"./ncnnoptimize yolov5-lite.param yolov5-lite.bin yolov5-lite-opt.param yolov5-lite-opt.bin 65536"。\[1\]在转换完成后,可以使用Oenvino框架进行推理,而不需要依赖pytorch等库。可以直接复制"利用Oenvino推理"的代码来进行推理。\[2\]最后,可以使用命令"./ncnn2table"将模型转换为table格式,例如"./ncnn2table yolov5-lite-opt.param yolov5-lite-opt.bin imagelist.txt yolov5-lite.table mean=\[104,117,123\] norm=\[0.017,0.017,0.017\] shape=\[640,640,3\] pixel=BGR thread=8 method=kl"。\[3\]如果需要进行int8量化,可以使用命令"./ncnn2int8"来进行转换,例如"./ncnn2int8 yolov5-lite-opt.param yolov5-lite-opt.bin yolov5-ite-opt-int8.param yolov5-lite-opt-int8.bin yolov5-lite.table"。\[3\]这样就完成了yolov5-lite模型的转换过程。
#### 引用[.reference_title]
- *1* *3* [YOLOv5-Lite:NCNN+Int8部署和量化,树莓派也可实时](https://blog.csdn.net/weixin_45829462/article/details/119787840)[target="_blank" data-report-click={"spm":"1018.2226.3001.9630","extra":{"utm_source":"vip_chatgpt_common_search_pc_result","utm_medium":"distribute.pc_search_result.none-task-cask-2~all~insert_cask~default-1-null.142^v91^control_2,239^v3^insert_chatgpt"}} ] [.reference_item]
- *2* [(YOLOv5-lite)-ONNX模型转换及Openvino推理](https://blog.csdn.net/weixin_45930948/article/details/124359754)[target="_blank" data-report-click={"spm":"1018.2226.3001.9630","extra":{"utm_source":"vip_chatgpt_common_search_pc_result","utm_medium":"distribute.pc_search_result.none-task-cask-2~all~insert_cask~default-1-null.142^v91^control_2,239^v3^insert_chatgpt"}} ] [.reference_item]
[ .reference_list ]
树莓派yolov5-lite安装
安装YOLOv5-Lite在树莓派上可以按照以下步骤进行:
1. 首先,确保你的树莓派已经连接到互联网,并且已经安装了Python环境。
2. 打开终端,使用以下命令克隆YOLOv5仓库到你的树莓派上:
```
git clone https://github.com/ultralytics/yolov5.git
```
3. 进入yolov5目录:
```
cd yolov5
```
4. 安装所需的Python依赖:
```
pip install -r requirements.txt
```
5. 下载预训练的权重文件:
```
wget https://github.com/ultralytics/yolov5/releases/download/v3.1/yolov5s.pt
```
6. 使用以下命令运行YOLOv5-Lite:
```
python detect.py --weights yolov5s.pt --img 640 --conf 0.25
```
这将使用预训练的权重文件在输入图像上运行YOLOv5-Lite,并将结果输出到终端。
请注意,YOLOv5-Lite相对于完整版的YOLOv5模型具有较小的体积和较低的精度。如果你需要更高的精度,可以尝试下载其他版本的权重文件(如yolov5m.pt或yolov5l.pt),并将其用于上述命令中。
希望这能帮助到你!如有任何问题,请随时提问。