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PyCharm Docs Inspect fixes (#18432)

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Signed-off-by: Glenn Jocher <glenn.jocher@ultralytics.com>
Co-authored-by: UltralyticsAssistant <web@ultralytics.com>
Co-authored-by: Glenn Jocher <glenn.jocher@ultralytics.com>
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Muhammad Rizwan Munawar 2024-12-29 23:19:05 +05:00 • committed by GitHub
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10
docs/en/usage/simple-utilities.md
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@ -32,7 +32,7 @@ Dataset annotation is a very resource intensive and time-consuming process. If y
```{ .py .annotate }
from ultralytics.data.annotator import auto_annotate
auto_annotate( # (1)!
auto_annotate(
data="path/to/new/data",
det_model="yolo11n.pt",
sam_model="mobile_sam.pt",
@ -41,17 +41,16 @@ auto_annotate( # (1)!
)
```
1. Nothing returns from this function
This function does not return any value. For further details on how the function operates:
- [See the reference section for `annotator.auto_annotate`](../reference/data/annotator.md#ultralytics.data.annotator.auto_annotate) for more insight on how the function operates.
- Use in combination with the [function `segments2boxes`](#convert-segments-to-bounding-boxes) to generate object detection bounding boxes as well
### Convert Segmentation Masks into YOLO Format
![Segmentation Masks to YOLO Format](https://github.com/ultralytics/docs/releases/download/0/segmentation-masks-to-yolo-format.avif)
Use to convert a dataset of segmentation mask images to the `YOLO` segmentation format.
Use to convert a dataset of segmentation mask images to the [`YOLO`](../models/yolo11.md) segmentation format.
This function takes the directory containing the binary format mask images and converts them into YOLO segmentation format.
The converted masks will be saved in the specified output directory.
@ -59,7 +58,8 @@ The converted masks will be saved in the specified output directory.
```python
from ultralytics.data.converter import convert_segment_masks_to_yolo_seg
# The classes here is the total classes in the dataset, for COCO dataset we have 80 classes
# The classes here is the total classes in the dataset.
# for COCO dataset we have 80 classes.
convert_segment_masks_to_yolo_seg(masks_dir="path/to/masks_dir", output_dir="path/to/output_dir", classes=80)
```