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ultralytics 8.0.136 refactor and simplify package (#3748)

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Co-authored-by: pre-commit-ci[bot] <66853113+pre-commit-ci[bot]@users.noreply.github.com>
Co-authored-by: Glenn Jocher <glenn.jocher@ultralytics.com>
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Laughing 2023-07-16 23:47:45 +08:00 • committed by GitHub
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docs/datasets/segment/index.md
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@ -104,7 +104,7 @@ If you have your own dataset and would like to use it for training segmentation
You can easily convert labels from the popular COCO dataset format to the YOLO format using the following code snippet:
```python
from ultralytics.yolo.data.converter import convert_coco
from ultralytics.data.converter import convert_coco
convert_coco(labels_dir='../coco/annotations/', use_segments=True)
```
@ -122,7 +122,7 @@ Auto-annotation is an essential feature that allows you to generate a segmentati
To auto-annotate your dataset using the Ultralytics framework, you can use the `auto_annotate` function as shown below:
```python
from ultralytics.yolo.data.annotator import auto_annotate
from ultralytics.data.annotator import auto_annotate
auto_annotate(data="path/to/images", det_model="yolov8x.pt", sam_model='sam_b.pt')
```