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Reformat Markdown code blocks (#12795)

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Signed-off-by: Glenn Jocher <glenn.jocher@ultralytics.com>
Co-authored-by: UltralyticsAssistant <web@ultralytics.com>
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Glenn Jocher 2024-05-18 18:58:06 +02:00 • committed by GitHub
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8
docs/en/datasets/segment/index.md
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@ -74,10 +74,10 @@ The `train` and `val` fields specify the paths to the directories containing the
from ultralytics import YOLO
# Load a model
model = YOLO('yolov8n-seg.pt') # load a pretrained model (recommended for training)
model = YOLO("yolov8n-seg.pt") # load a pretrained model (recommended for training)
# Train the model
results = model.train(data='coco8-seg.yaml', epochs=100, imgsz=640)
results = model.train(data="coco8-seg.yaml", epochs=100, imgsz=640)
```
=== "CLI"
@ -117,7 +117,7 @@ You can easily convert labels from the popular COCO dataset format to the YOLO f
```python
from ultralytics.data.converter import convert_coco
convert_coco(labels_dir='path/to/coco/annotations/', use_segments=True)
convert_coco(labels_dir="path/to/coco/annotations/", use_segments=True)
```
This conversion tool can be used to convert the COCO dataset or any dataset in the COCO format to the Ultralytics YOLO format.
@ -139,7 +139,7 @@ To auto-annotate your dataset using the Ultralytics framework, you can use the `
```python
from ultralytics.data.annotator import auto_annotate
auto_annotate(data="path/to/images", det_model="yolov8x.pt", sam_model='sam_b.pt')
auto_annotate(data="path/to/images", det_model="yolov8x.pt", sam_model="sam_b.pt")
```
Certainly, here is the table updated with code snippets: