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

Argument	Type	Default	Description
source	str	'ultralytics/assets'	Specifies the data source for inference. Can be an image path, video file, directory, URL, or device ID for live feeds. Supports a wide range of formats and sources, enabling flexible application across different types of input.
conf	float	0.25	Sets the minimum confidence threshold for detections. Objects detected with confidence below this threshold will be disregarded. Adjusting this value can help reduce false positives.
iou	float	0.7	Intersection Over Union (IoU) threshold for Non-Maximum Suppression (NMS). Lower values result in fewer detections by eliminating overlapping boxes, useful for reducing duplicates.
imgsz	int or tuple	640	Defines the image size for inference. Can be a single integer 640 for square resizing or a (height, width) tuple. Proper sizing can improve detection accuracy and processing speed.
half	bool	False	Enables half-precision (FP16) inference, which can speed up model inference on supported GPUs with minimal impact on accuracy.
device	str	None	Specifies the device for inference (e.g., cpu, cuda:0 or 0). Allows users to select between CPU, a specific GPU, or other compute devices for model execution.
batch	int	1	Specifies the batch size for inference (only works when the source is a directory, video file or .txt file). A larger batch size can provide higher throughput, shortening the total amount of time required for inference.
max_det	int	300	Maximum number of detections allowed per image. Limits the total number of objects the model can detect in a single inference, preventing excessive outputs in dense scenes.
vid_stride	int	1	Frame stride for video inputs. Allows skipping frames in videos to speed up processing at the cost of temporal resolution. A value of 1 processes every frame, higher values skip frames.
stream_buffer	bool	False	Determines whether to queue incoming frames for video streams. If False, old frames get dropped to accommodate new frames (optimized for real-time applications). If True, queues new frames in a buffer, ensuring no frames get skipped, but will cause latency if inference FPS is lower than stream FPS.
visualize	bool	False	Activates visualization of model features during inference, providing insights into what the model is "seeing". Useful for debugging and model interpretation.
augment	bool	False	Enables test-time augmentation (TTA) for predictions, potentially improving detection robustness at the cost of inference speed.
agnostic_nms	bool	False	Enables class-agnostic Non-Maximum Suppression (NMS), which merges overlapping boxes of different classes. Useful in multi-class detection scenarios where class overlap is common.
classes	list[int]	None	Filters predictions to a set of class IDs. Only detections belonging to the specified classes will be returned. Useful for focusing on relevant objects in multi-class detection tasks.
retina_masks	bool	False	Returns high-resolution segmentation masks. The returned masks (masks.data) will match the original image size if enabled. If disabled, they have the image size used during inference.
embed	list[int]	None	Specifies the layers from which to extract feature vectors or embeddings. Useful for downstream tasks like clustering or similarity search.
project	str	None	Name of the project directory where prediction outputs are saved if save is enabled.
name	str	None	Name of the prediction run. Used for creating a subdirectory within the project folder, where prediction outputs are stored if save is enabled.
stream	bool	False	Enables memory-efficient processing for long videos or numerous images by returning a generator of Results objects instead of loading all frames into memory at once.
verbose	bool	True	Controls whether to display detailed inference logs in the terminal, providing real-time feedback on the prediction process.