YOLO中model.val()方法参数解释

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已于 2025-04-06 20:07:30 修改 · 1k 阅读

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   Argument 
   Type 
   Default 
   Description 
   Explain 
  
   data 
   str 
   None 
   Specifies the path to the dataset configuration file (e.g., coco8.yaml). This file includes paths to validation data, class names, and number of classes. 
    
   imgsz 
   int 
   640 
   Defines the size of input images. All images are resized to this dimension before processing. Larger sizes may improve accuracy for small objects but increase computation time. 
    
   batch 
   int 
   16 
   Sets the number of images per batch. Higher values utilize GPU memory more efficiently but require more VRAM. Adjust based on available hardware resources. 
    
   save_json 
   bool 
   False 
   If True, saves the results to a JSON file for further analysis, integration with other tools, or submission to evaluation servers like COCO. 
   如果为真（True），则将结果保存到一个JSON文件中，以便进行进一步的分析、与其他工具集成，或者提交到诸如COCO这样的评估服务器上。 
  
   save_hybrid 
   bool 
   False 
   If True, saves a hybrid version of labels that combines original annotations with additional model predictions. Useful for semi-supervised learning and dataset

Argument	Type	Default	Description	Explain
data	str	None	Specifies the path to the dataset configuration file (e.g., coco8.yaml). This file includes paths to validation data, class names, and number of classes.
imgsz	int	640	Defines the size of input images. All images are resized to this dimension before processing. Larger sizes may improve accuracy for small objects but increase computation time.
batch	int	16	Sets the number of images per batch. Higher values utilize GPU memory more efficiently but require more VRAM. Adjust based on available hardware resources.
save_json	bool	False	If True, saves the results to a JSON file for further analysis, integration with other tools, or submission to evaluation servers like COCO.	如果为真（True），则将结果保存到一个JSON文件中，以便进行进一步的分析、与其他工具集成，或者提交到诸如COCO这样的评估服务器上。
save_hybrid	bool	False	If True, saves a hybrid version of labels that combines original annotations with additional model predictions. Useful for semi-supervised learning and dataset