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Add Docs glossary links (#16448)

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Signed-off-by: UltralyticsAssistant <web@ultralytics.com>
Co-authored-by: UltralyticsAssistant <web@ultralytics.com>
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docs/en/tasks/classify.md
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@ -9,7 +9,7 @@ model_name: yolov8n-cls
<img width="1024" src="https://github.com/ultralytics/docs/releases/download/0/image-classification-examples.avif" alt="Image classification examples">
Image classification is the simplest of the three tasks and involves classifying an entire image into one of a set of predefined classes.
[Image classification](https://www.ultralytics.com/glossary/image-classification) is the simplest of the three tasks and involves classifying an entire image into one of a set of predefined classes.
The output of an image classifier is a single class label and a confidence score. Image classification is useful when you need to know only what class an image belongs to and don't need to know where objects of that class are located or what their exact shape is.
@ -47,7 +47,7 @@ YOLOv8 pretrained Classify models are shown here. Detect, Segment and Pose model
## Train
Train YOLOv8n-cls on the MNIST160 dataset for 100 epochs at image size 64. For a full list of available arguments see the [Configuration](../usage/cfg.md) page.
Train YOLOv8n-cls on the MNIST160 dataset for 100 [epochs](https://www.ultralytics.com/glossary/epoch) at image size 64. For a full list of available arguments see the [Configuration](../usage/cfg.md) page.
!!! example
@ -84,7 +84,7 @@ YOLO classification dataset format can be found in detail in the [Dataset Guide]
## Val
Validate trained YOLOv8n-cls model accuracy on the MNIST160 dataset. No arguments are needed as the `model` retains its training `data` and arguments as model attributes.
Validate trained YOLOv8n-cls model [accuracy](https://www.ultralytics.com/glossary/accuracy) on the MNIST160 dataset. No arguments are needed as the `model` retains its training `data` and arguments as model attributes.
!!! example