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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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18
docs/en/modes/train.md
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@ -59,12 +59,12 @@ Train YOLOv8n on the COCO8 dataset for 100 epochs at image size 640. The trainin
from ultralytics import YOLO
# Load a model
model = YOLO('yolov8n.yaml') # build a new model from YAML
model = YOLO('yolov8n.pt') # load a pretrained model (recommended for training)
model = YOLO('yolov8n.yaml').load('yolov8n.pt') # build from YAML and transfer weights
model = YOLO("yolov8n.yaml") # build a new model from YAML
model = YOLO("yolov8n.pt") # load a pretrained model (recommended for training)
model = YOLO("yolov8n.yaml").load("yolov8n.pt") # build from YAML and transfer weights
# Train the model
results = model.train(data='coco8.yaml', epochs=100, imgsz=640)
results = model.train(data="coco8.yaml", epochs=100, imgsz=640)
```
=== "CLI"
@ -94,10 +94,10 @@ Multi-GPU training allows for more efficient utilization of available hardware r
from ultralytics import YOLO
# Load a model
model = YOLO('yolov8n.pt') # load a pretrained model (recommended for training)
model = YOLO("yolov8n.pt") # load a pretrained model (recommended for training)
# Train the model with 2 GPUs
results = model.train(data='coco8.yaml', epochs=100, imgsz=640, device=[0, 1])
results = model.train(data="coco8.yaml", epochs=100, imgsz=640, device=[0, 1])
```
=== "CLI"
@ -121,10 +121,10 @@ To enable training on Apple M1 and M2 chips, you should specify 'mps' as your de
from ultralytics import YOLO
# Load a model
model = YOLO('yolov8n.pt') # load a pretrained model (recommended for training)
model = YOLO("yolov8n.pt") # load a pretrained model (recommended for training)
# Train the model with 2 GPUs
results = model.train(data='coco8.yaml', epochs=100, imgsz=640, device='mps')
results = model.train(data="coco8.yaml", epochs=100, imgsz=640, device="mps")
```
=== "CLI"
@ -154,7 +154,7 @@ Below is an example of how to resume an interrupted training using Python and vi
from ultralytics import YOLO
# Load a model
model = YOLO('path/to/last.pt') # load a partially trained model
model = YOLO("path/to/last.pt") # load a partially trained model
# Resume training
results = model.train(resume=True)