yolov7的v5 metric
时间: 2023-09-19 09:11:02 浏览: 71
关于yolov7的v5 metric,根据提供的引用内容中没有具体描述yolov7的v5 metric的信息。在给出确切答案之前,还需要提供关于yolov7的v5 metric的更多信息。<em>1</em><em>2</em><em>3</em>
#### 引用[.reference_title]
- *1* *2* *3* [Yolov7训练自己的数据-水果检测](https://blog.csdn.net/m0_67089871/article/details/128686711)[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^v92^chatsearchT3_1"}} ] [.reference_item]
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相关问题
yolov5 int8量化
为了对YOLOv5模型进行INT8量化,可以按照以下步骤进行操作:
1. 首先,使用OpenVINO backend运行YOLOv5m INT8模型推理demo。可以使用以下命令行来运行demo:
```
cd yolov5 && python detect.py --weights ./yolov5m/yolov5m_openvino_model/optimized/yolov5m.xml
```
2. 使用Python的export.py脚本来导出YOLOv5m模型。可以使用以下命令行导出模型:
```
python export.py --weights yolov5m/yolov5m.pt --imgsz 640
```
3. 由于YOLOv5模型的前后处理模块与OpenVINO Accuracy Checker Tool预定义的前后处理模块不完全一致,因此需要采用基于POT API调用方式来实现YOLOv5的模型INT8量化。可以按照以下步骤进行量化:
- 基于POT API调用方式,集成自定义的DataLoader和Metric到量化流水线中。
- 使用量化流水线对YOLOv5模型进行量化。
通过以上步骤,可以成功对YOLOv5模型进行INT8量化。
TypeError: test() got an unexpected keyword argument 'v5_metric'
As an AI language model, I cannot provide a specific solution without the full context of the code. However, this error message usually occurs when a function or method is called with an unexpected keyword argument. The function or method may not have been defined to accept this argument, or the argument may have been misspelled or passed in incorrectly.
To resolve this error, you can check the definition of the function or method to ensure it accepts the keyword argument 'v5_metric', and also verify that the argument is being passed in correctly.