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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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10
docs/en/datasets/classify/imagewoof.md
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@ -34,10 +34,10 @@ To train a CNN model on the ImageWoof dataset for 100 epochs with an image size
from ultralytics import YOLO
# Load a model
model = YOLO('yolov8n-cls.pt') # load a pretrained model (recommended for training)
model = YOLO("yolov8n-cls.pt") # load a pretrained model (recommended for training)
# Train the model
results = model.train(data='imagewoof', epochs=100, imgsz=224)
results = model.train(data="imagewoof", epochs=100, imgsz=224)
```
=== "CLI"
@ -63,13 +63,13 @@ To use these variants in your training, simply replace 'imagewoof' in the datase
from ultralytics import YOLO
# Load a model
model = YOLO('yolov8n-cls.pt') # load a pretrained model (recommended for training)
model = YOLO("yolov8n-cls.pt") # load a pretrained model (recommended for training)
# For medium-sized dataset
model.train(data='imagewoof320', epochs=100, imgsz=224)
model.train(data="imagewoof320", epochs=100, imgsz=224)
# For small-sized dataset
model.train(data='imagewoof160', epochs=100, imgsz=224)
model.train(data="imagewoof160", epochs=100, imgsz=224)
```
It's important to note that using smaller images will likely yield lower performance in terms of classification accuracy. However, it's an excellent way to iterate quickly in the early stages of model development and prototyping.