| 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.                                                                 |
| `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 if all frames should be buffered when processing video streams (`True`), or if the model should return the most recent frame (`False`). Useful for real-time applications.                                                |
| `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`                | Uses high-resolution segmentation masks if available in the model. This can enhance mask quality for segmentation tasks, providing finer detail.                                                                                     |
| `embed`         | `list[int]`    | `None`                 | Specifies the layers from which to extract feature vectors or embeddings. Useful for downstream tasks like clustering or similarity search.                                                                                          |