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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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docs/en/tasks/classify.md
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@ -56,12 +56,12 @@ Train YOLOv8n-cls on the MNIST160 dataset for 100 epochs at image size 64. For a
from ultralytics import YOLO
# Load a model
model = YOLO('yolov8n-cls.yaml') # build a new model from YAML
model = YOLO('yolov8n-cls.pt') # load a pretrained model (recommended for training)
model = YOLO('yolov8n-cls.yaml').load('yolov8n-cls.pt') # build from YAML and transfer weights
model = YOLO("yolov8n-cls.yaml") # build a new model from YAML
model = YOLO("yolov8n-cls.pt") # load a pretrained model (recommended for training)
model = YOLO("yolov8n-cls.yaml").load("yolov8n-cls.pt") # build from YAML and transfer weights
# Train the model
results = model.train(data='mnist160', epochs=100, imgsz=64)
results = model.train(data="mnist160", epochs=100, imgsz=64)
```
=== "CLI"
@ -93,13 +93,13 @@ Validate trained YOLOv8n-cls model accuracy on the MNIST160 dataset. No argument
from ultralytics import YOLO
# Load a model
model = YOLO('yolov8n-cls.pt') # load an official model
model = YOLO('path/to/best.pt') # load a custom model
model = YOLO("yolov8n-cls.pt") # load an official model
model = YOLO("path/to/best.pt") # load a custom model
# Validate the model
metrics = model.val() # no arguments needed, dataset and settings remembered
metrics.top1 # top1 accuracy
metrics.top5 # top5 accuracy
metrics.top1 # top1 accuracy
metrics.top5 # top5 accuracy
```
=== "CLI"
@ -120,11 +120,11 @@ Use a trained YOLOv8n-cls model to run predictions on images.
from ultralytics import YOLO
# Load a model
model = YOLO('yolov8n-cls.pt') # load an official model
model = YOLO('path/to/best.pt') # load a custom model
model = YOLO("yolov8n-cls.pt") # load an official model
model = YOLO("path/to/best.pt") # load a custom model
# Predict with the model
results = model('https://ultralytics.com/images/bus.jpg') # predict on an image
results = model("https://ultralytics.com/images/bus.jpg") # predict on an image
```
=== "CLI"
@ -147,11 +147,11 @@ Export a YOLOv8n-cls model to a different format like ONNX, CoreML, etc.
from ultralytics import YOLO
# Load a model
model = YOLO('yolov8n-cls.pt') # load an official model
model = YOLO('path/to/best.pt') # load a custom trained model
model = YOLO("yolov8n-cls.pt") # load an official model
model = YOLO("path/to/best.pt") # load a custom trained model
# Export the model
model.export(format='onnx')
model.export(format="onnx")
```
=== "CLI"