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Update https://docs.ultralytics.com/models (#6513)

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Signed-off-by: Glenn Jocher <glenn.jocher@ultralytics.com>
Co-authored-by: pre-commit-ci[bot] <66853113+pre-commit-ci[bot]@users.noreply.github.com>
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Glenn Jocher 2023-11-22 20:45:46 +01:00 • committed by GitHub
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docs/en/datasets/segment/coco.md
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@ -76,7 +76,7 @@ The example showcases the variety and complexity of the images in the COCO-Seg d
If you use the COCO-Seg dataset in your research or development work, please cite the original COCO paper and acknowledge the extension to COCO-Seg:
!!! Note ""
!!! Quote ""
=== "BibTeX"

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docs/en/datasets/segment/coco8-seg.md
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@ -62,7 +62,7 @@ The example showcases the variety and complexity of the images in the COCO8-Seg
If you use the COCO dataset in your research or development work, please cite the following paper:
!!! Note ""
!!! Quote ""
=== "BibTeX"

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docs/en/datasets/segment/index.md
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@ -66,7 +66,7 @@ The `train` and `val` fields specify the paths to the directories containing the
## Usage
!!! Example ""
!!! Example
=== "Python"
@ -101,7 +101,7 @@ 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:
!!! Example ""
!!! Example
=== "Python"
@ -123,7 +123,7 @@ 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:
!!! Example ""
!!! Example
=== "Python"