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updated the amount of macros available in the macros folder (#16086)

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Co-authored-by: UltralyticsAssistant <web@ultralytics.com>
Co-authored-by: Francesco Mattioli <Francesco.mttl@gmail.com>
Co-authored-by: Glenn Jocher <glenn.jocher@ultralytics.com>
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docs/en/modes/predict.md
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@ -365,38 +365,11 @@ Below are code examples for using each source type:
Inference arguments:
| 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. |
{% include "macros/predict-args.md" %}
Visualization arguments:
| Argument | Type | Default | Description |
| ------------- | --------------- | ----------------- | -------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- |
| `show` | `bool` | `False` | If `True`, displays the annotated images or videos in a window. Useful for immediate visual feedback during development or testing. |
| `save` | `bool` | `False` or `True` | Enables saving of the annotated images or videos to file. Useful for documentation, further analysis, or sharing results. Defaults to True when using CLI & False when used in Python. |
| `save_frames` | `bool` | `False` | When processing videos, saves individual frames as images. Useful for extracting specific frames or for detailed frame-by-frame analysis. |
| `save_txt` | `bool` | `False` | Saves detection results in a text file, following the format `[class] [x_center] [y_center] [width] [height] [confidence]`. Useful for integration with other analysis tools. |
| `save_conf` | `bool` | `False` | Includes confidence scores in the saved text files. Enhances the detail available for post-processing and analysis. |
| `save_crop` | `bool` | `False` | Saves cropped images of detections. Useful for dataset augmentation, analysis, or creating focused datasets for specific objects. |
| `show_labels` | `bool` | `True` | Displays labels for each detection in the visual output. Provides immediate understanding of detected objects. |
| `show_conf` | `bool` | `True` | Displays the confidence score for each detection alongside the label. Gives insight into the model's certainty for each detection. |
| `show_boxes` | `bool` | `True` | Draws bounding boxes around detected objects. Essential for visual identification and location of objects in images or video frames. |
| `line_width` | `None` or `int` | `None` | Specifies the line width of bounding boxes. If `None`, the line width is automatically adjusted based on the image size. Provides visual customization for clarity. |
{% include "macros/visualization-args.md" %}
## Image and Video Formats