ultralytics/docs/en/macros/validation-args.md

Argument	Type	Default	Description
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.
conf	float	0.001	Sets the minimum confidence threshold for detections. Lower values increase recall but may introduce more false positives. Used during validation to compute precision-recall curves.
iou	float	0.7	Sets the Intersection Over Union threshold for Non-Maximum Suppression. Controls duplicate detection elimination.
max_det	int	300	Limits the maximum number of detections per image. Useful in dense scenes to prevent excessive detections and manage computational resources.
half	bool	True	Enables half-precision (FP16) computation, reducing memory usage and potentially increasing speed with minimal impact on accuracy.
device	str	None	Specifies the device for validation (cpu, cuda:0, etc.). When None, automatically selects the best available device. Multiple CUDA devices can be specified with comma separation.
dnn	bool	False	If True, uses the OpenCV DNN module for ONNX model inference, offering an alternative to PyTorch inference methods.
plots	bool	False	When set to True, generates and saves plots of predictions versus ground truth, confusion matrices, and PR curves for visual evaluation of model performance.
classes	list[int]	None	Specifies a list of class IDs to train on. Useful for filtering out and focusing only on certain classes during evaluation.
rect	bool	True	If True, uses rectangular inference for batching, reducing padding and potentially increasing speed and efficiency by processing images in their original aspect ratio.
split	str	'val'	Determines the dataset split to use for validation (val, test, or train). Allows flexibility in choosing the data segment for performance evaluation.
project	str	None	Name of the project directory where validation outputs are saved. Helps organize results from different experiments or models.
name	str	None	Name of the validation run. Used for creating a subdirectory within the project folder, where validation logs and outputs are stored.
verbose	bool	False	If True, displays detailed information during the validation process, including per-class metrics, batch progress, and additional debugging information.
save_txt	bool	False	If True, saves detection results in text files, with one file per image, useful for further analysis, custom post-processing, or integration with other systems.
save_conf	bool	False	If True, includes confidence values in the saved text files when save_txt is enabled, providing more detailed output for analysis and filtering.
workers	int	8	Number of worker threads for data loading. Higher values can speed up data preprocessing but may increase CPU usage. Setting to 0 uses main thread, which can be more stable in some environments.
augment	bool	False	Enables test-time augmentation (TTA) during validation, potentially improving detection accuracy at the cost of inference speed by running inference on transformed versions of the input.
agnostic_nms	bool	False	Enables class-agnostic Non-Maximum Suppression, which merges overlapping boxes regardless of their predicted class. Useful for instance-focused applications.
single_cls	bool	False	Treats all classes as a single class during validation. Useful for evaluating model performance on binary detection tasks or when class distinctions aren't important.
visualize	bool	False	Visualizes the ground truths, true positives, false positives and false negatives for each image. Useful for debugging and model interpretation.