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Standardize str formatting in docs (#19276)

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Co-authored-by: UltralyticsAssistant <web@ultralytics.com>
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40
docs/en/macros/augmentation-args.md
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| Argument | Type | Default | Range | Description |
| ----------------- | ------- | ------------- | ------------- | ------------------------------------------------------------------------------------------------------------------------------------------------------------------------- |
| `hsv_h` | `float` | `0.015` | `0.0 - 1.0` | Adjusts the hue of the image by a fraction of the color wheel, introducing color variability. Helps the model generalize across different lighting conditions. |
| `hsv_s` | `float` | `0.7` | `0.0 - 1.0` | Alters the saturation of the image by a fraction, affecting the intensity of colors. Useful for simulating different environmental conditions. |
| `hsv_v` | `float` | `0.4` | `0.0 - 1.0` | Modifies the value (brightness) of the image by a fraction, helping the model to perform well under various lighting conditions. |
| `degrees` | `float` | `0.0` | `-180 - +180` | Rotates the image randomly within the specified degree range, improving the model's ability to recognize objects at various orientations. |
| `translate` | `float` | `0.1` | `0.0 - 1.0` | Translates the image horizontally and vertically by a fraction of the image size, aiding in learning to detect partially visible objects. |
| `scale` | `float` | `0.5` | `>=0.0` | Scales the image by a gain factor, simulating objects at different distances from the camera. |
| `shear` | `float` | `0.0` | `-180 - +180` | Shears the image by a specified degree, mimicking the effect of objects being viewed from different angles. |
| `perspective` | `float` | `0.0` | `0.0 - 0.001` | Applies a random perspective transformation to the image, enhancing the model's ability to understand objects in 3D space. |
| `flipud` | `float` | `0.0` | `0.0 - 1.0` | Flips the image upside down with the specified probability, increasing the data variability without affecting the object's characteristics. |
| `fliplr` | `float` | `0.5` | `0.0 - 1.0` | Flips the image left to right with the specified probability, useful for learning symmetrical objects and increasing dataset diversity. |
| `bgr` | `float` | `0.0` | `0.0 - 1.0` | Flips the image channels from RGB to BGR with the specified probability, useful for increasing robustness to incorrect channel ordering. |
| `mosaic` | `float` | `1.0` | `0.0 - 1.0` | Combines four training images into one, simulating different scene compositions and object interactions. Highly effective for complex scene understanding. |
| `mixup` | `float` | `0.0` | `0.0 - 1.0` | Blends two images and their labels, creating a composite image. Enhances the model's ability to generalize by introducing label noise and visual variability. |
| `copy_paste` | `float` | `0.0` | `0.0 - 1.0` | Copies and pastes objects across images, useful for increasing object instances and learning object occlusion. Requires segmentation labels. |
| `copy_paste_mode` | `str` | `flip` | - | Copy-Paste augmentation method selection among the options of (`"flip"`, `"mixup"`). |
| `auto_augment` | `str` | `randaugment` | - | Automatically applies a predefined augmentation policy (`randaugment`, `autoaugment`, `augmix`), optimizing for classification tasks by diversifying the visual features. |
| `erasing` | `float` | `0.4` | `0.0 - 0.9` | Randomly erases a portion of the image during classification training, encouraging the model to focus on less obvious features for recognition. |
| `crop_fraction` | `float` | `1.0` | `0.1 - 1.0` | Crops the classification image to a fraction of its size to emphasize central features and adapt to object scales, reducing background distractions. |
| Argument | Type | Default | Range | Description |
| ----------------- | ------- | --------------- | ------------- | ------------------------------------------------------------------------------------------------------------------------------------------------------------------------- |
| `hsv_h` | `float` | `0.015` | `0.0 - 1.0` | Adjusts the hue of the image by a fraction of the color wheel, introducing color variability. Helps the model generalize across different lighting conditions. |
| `hsv_s` | `float` | `0.7` | `0.0 - 1.0` | Alters the saturation of the image by a fraction, affecting the intensity of colors. Useful for simulating different environmental conditions. |
| `hsv_v` | `float` | `0.4` | `0.0 - 1.0` | Modifies the value (brightness) of the image by a fraction, helping the model to perform well under various lighting conditions. |
| `degrees` | `float` | `0.0` | `-180 - +180` | Rotates the image randomly within the specified degree range, improving the model's ability to recognize objects at various orientations. |
| `translate` | `float` | `0.1` | `0.0 - 1.0` | Translates the image horizontally and vertically by a fraction of the image size, aiding in learning to detect partially visible objects. |
| `scale` | `float` | `0.5` | `>=0.0` | Scales the image by a gain factor, simulating objects at different distances from the camera. |
| `shear` | `float` | `0.0` | `-180 - +180` | Shears the image by a specified degree, mimicking the effect of objects being viewed from different angles. |
| `perspective` | `float` | `0.0` | `0.0 - 0.001` | Applies a random perspective transformation to the image, enhancing the model's ability to understand objects in 3D space. |
| `flipud` | `float` | `0.0` | `0.0 - 1.0` | Flips the image upside down with the specified probability, increasing the data variability without affecting the object's characteristics. |
| `fliplr` | `float` | `0.5` | `0.0 - 1.0` | Flips the image left to right with the specified probability, useful for learning symmetrical objects and increasing dataset diversity. |
| `bgr` | `float` | `0.0` | `0.0 - 1.0` | Flips the image channels from RGB to BGR with the specified probability, useful for increasing robustness to incorrect channel ordering. |
| `mosaic` | `float` | `1.0` | `0.0 - 1.0` | Combines four training images into one, simulating different scene compositions and object interactions. Highly effective for complex scene understanding. |
| `mixup` | `float` | `0.0` | `0.0 - 1.0` | Blends two images and their labels, creating a composite image. Enhances the model's ability to generalize by introducing label noise and visual variability. |
| `copy_paste` | `float` | `0.0` | `0.0 - 1.0` | Copies and pastes objects across images, useful for increasing object instances and learning object occlusion. Requires segmentation labels. |
| `copy_paste_mode` | `str` | `'flip'` | - | Copy-Paste augmentation method selection among the options of (`"flip"`, `"mixup"`). |
| `auto_augment` | `str` | `'randaugment'` | - | Automatically applies a predefined augmentation policy (`randaugment`, `autoaugment`, `augmix`), optimizing for classification tasks by diversifying the visual features. |
| `erasing` | `float` | `0.4` | `0.0 - 0.9` | Randomly erases a portion of the image during classification training, encouraging the model to focus on less obvious features for recognition. |
| `crop_fraction` | `float` | `1.0` | `0.1 - 1.0` | Crops the classification image to a fraction of its size to emphasize central features and adapt to object scales, reducing background distractions. |

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docs/en/macros/export-args.md
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| `nms` | `bool` | `False` | Adds Non-Maximum Suppression (NMS) to the exported model when supported (see Export Formats), improving detection post-processing efficiency. |
| `batch` | `int` | `1` | Specifies export model batch inference size or the max number of images the exported model will process concurrently in `predict` mode. |
| `device` | `str` | `None` | Specifies the device for exporting: GPU (`device=0`), CPU (`device=cpu`), MPS for Apple silicon (`device=mps`) or DLA for NVIDIA Jetson (`device=dla:0` or `device=dla:1`). |
| `data` | `str` | `coco8.yaml` | Path to the [dataset](https://docs.ultralytics.com/datasets) configuration file (default: `coco8.yaml`), essential for quantization. |
| `data` | `str` | `'coco8.yaml'` | Path to the [dataset](https://docs.ultralytics.com/datasets) configuration file (default: `coco8.yaml`), essential for quantization. |

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docs/en/macros/sam-auto-annotate.md
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| Argument | Type | Default | Description |
| ------------ | ----------- | -------------- | ---------------------------------------------------------------------------------------------- |
| `data` | `str` | required | Path to directory containing target images for annotation or segmentation. |
| `det_model` | `str` | `"yolo11x.pt"` | YOLO detection model path for initial object detection. |
| `sam_model` | `str` | `"sam2_b.pt"` | SAM2 model path for segmentation (supports t/s/b/l variants and SAM2.1) and mobile_sam models. |
| `device` | `str` | `""` | Computation device (e.g., 'cuda:0', 'cpu', or '' for automatic device detection). |
| `det_model` | `str` | `'yolo11x.pt'` | YOLO detection model path for initial object detection. |
| `sam_model` | `str` | `'sam2_b.pt'` | SAM2 model path for segmentation (supports t/s/b/l variants and SAM2.1) and mobile_sam models. |
| `device` | `str` | `''` | Computation device (e.g., 'cuda:0', 'cpu', or '' for automatic device detection). |
| `conf` | `float` | `0.25` | YOLO detection confidence threshold for filtering weak detections. |
| `iou` | `float` | `0.45` | IoU threshold for Non-Maximum Suppression to filter overlapping boxes. |
| `imgsz` | `int` | `640` | Input size for resizing images (must be multiple of 32). |

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docs/en/macros/solutions-args.md
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| `up_angle` | `float` | `145.0` | Angle threshold for detecting the "up" position in workouts monitoring. Can be adjusted based on the position of keypoints for different exercises. |
| `down_angle` | `float` | `90.0` | Angle threshold for detecting the "down" position in workouts monitoring. Adjust this based on keypoint positions for specific exercises. |
| `kpts` | `list` | `[6, 8, 10]` | List of keypoints used for monitoring workouts. These keypoints correspond to body joints or parts, such as shoulders, elbows, and wrists, for exercises like push-ups, pull-ups, squats, ab-workouts. |
| `analytics_type` | `str` | `line` | Specifies the type of analytics visualization to generate. Options include `"line"`, `"pie"`, `"bar"`, or `"area"`. The default is `"line"` for trend visualization. |
| `analytics_type` | `str` | `'line'` | Specifies the type of analytics visualization to generate. Options include `"line"`, `"pie"`, `"bar"`, or `"area"`. The default is `"line"` for trend visualization. |
| `json_file` | `str` | `None` | Path to the JSON file defining regions for parking systems or similar applications. Enables flexible configuration of analysis areas. |
| `records` | `int` | `5` | Total detections count that triggers an automated email notification about unusual activity. |

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docs/en/macros/track-args.md
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| Argument | Type | Default | Description |
| --------- | ------- | -------------- | ---------------------------------------------------------------------------------------------------------------------------------------------------------- |
| `source` | `str` | `None` | Specifies the source directory for images or videos. Supports file paths and URLs. |
| `persist` | `bool` | `False` | Enables persistent tracking of objects between frames, maintaining IDs across video sequences. |
| `tracker` | `str` | `botsort.yaml` | Specifies the tracking algorithm to use, e.g., `bytetrack.yaml` or `botsort.yaml`. |
| `conf` | `float` | `0.3` | Sets the confidence threshold for detections; lower values allow more objects to be tracked but may include false positives. |
| `iou` | `float` | `0.5` | Sets the [Intersection over Union](https://www.ultralytics.com/glossary/intersection-over-union-iou) (IoU) threshold for filtering overlapping detections. |
| `classes` | `list` | `None` | Filters results by class index. For example, `classes=[0, 2, 3]` only tracks the specified classes. |
| `verbose` | `bool` | `True` | Controls the display of tracking results, providing a visual output of tracked objects. |
| Argument | Type | Default | Description |
| --------- | ------- | ---------------- | ---------------------------------------------------------------------------------------------------------------------------------------------------------- |
| `source` | `str` | `None` | Specifies the source directory for images or videos. Supports file paths and URLs. |
| `persist` | `bool` | `False` | Enables persistent tracking of objects between frames, maintaining IDs across video sequences. |
| `tracker` | `str` | `'botsort.yaml'` | Specifies the tracking algorithm to use, e.g., `bytetrack.yaml` or `botsort.yaml`. |
| `conf` | `float` | `0.3` | Sets the confidence threshold for detections; lower values allow more objects to be tracked but may include false positives. |
| `iou` | `float` | `0.5` | Sets the [Intersection over Union](https://www.ultralytics.com/glossary/intersection-over-union-iou) (IoU) threshold for filtering overlapping detections. |
| `classes` | `list` | `None` | Filters results by class index. For example, `classes=[0, 2, 3]` only tracks the specified classes. |
| `verbose` | `bool` | `True` | Controls the display of tracking results, providing a visual output of tracked objects. |

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docs/en/macros/validation-args.md
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| `dnn` | `bool` | `False` | If `True`, uses the [OpenCV](https://www.ultralytics.com/glossary/opencv) DNN module for ONNX model inference, offering an alternative to [PyTorch](https://www.ultralytics.com/glossary/pytorch) inference methods. |
| `plots` | `bool` | `False` | When set to `True`, generates and saves plots of predictions versus ground truth for visual evaluation of the model's performance. |
| `rect` | `bool` | `True` | If `True`, uses rectangular inference for batching, reducing padding and potentially increasing speed and efficiency. |
| `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. |
| `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. |
| `name` | `str` | `None` | Name of the validation run. Used for creating a subdirectory within the project folder, where validation logs and outputs are stored. |