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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/guides/yolo-common-issues.md
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@ -79,7 +79,7 @@ This section will address common issues faced while training and their respectiv
- Make sure you pass the path to your `.yaml` file as the `data` argument when calling `model.train()`, as shown below:
```python
model.train(data='/path/to/your/data.yaml', batch=4)
model.train(data="/path/to/your/data.yaml", batch=4)
```
#### Accelerating Training with Multiple GPUs
@ -98,7 +98,7 @@ model.train(data='/path/to/your/data.yaml', batch=4)
```python
# Adjust the batch size and other settings as needed to optimize training speed
model.train(data='/path/to/your/data.yaml', batch=32, multi_scale=True)
model.train(data="/path/to/your/data.yaml", batch=32, multi_scale=True)
```
#### Continuous Monitoring Parameters
@ -221,10 +221,10 @@ yolo task=detect mode=segment model=yolov8n-seg.pt source='path/to/car.mp4' show
from ultralytics import YOLO
# Load a pre-trained YOLOv8 model
model = YOLO('yolov8n.pt')
model = YOLO("yolov8n.pt")
# Specify the source image
source = 'https://ultralytics.com/images/bus.jpg'
source = "https://ultralytics.com/images/bus.jpg"
# Make predictions
results = model.predict(source, save=True, imgsz=320, conf=0.5)