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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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6
docs/en/tasks/detect.md
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@ -8,7 +8,7 @@ keywords: object detection, YOLOv8, pretrained models, training, validation, pre
<img width="1024" src="https://github.com/ultralytics/docs/releases/download/0/object-detection-examples.avif" alt="Object detection examples">
Object detection is a task that involves identifying the location and class of objects in an image or video stream.
[Object detection](https://www.ultralytics.com/glossary/object-detection) is a task that involves identifying the location and class of objects in an image or video stream.
The output of an object detector is a set of bounding boxes that enclose the objects in the image, along with class labels and confidence scores for each box. Object detection is a good choice when you need to identify objects of interest in a scene, but don't need to know exactly where the object is or its exact shape.
@ -46,7 +46,7 @@ YOLOv8 pretrained Detect models are shown here. Detect, Segment and Pose models
## Train
Train YOLOv8n on the COCO8 dataset for 100 epochs at image size 640. For a full list of available arguments see the [Configuration](../usage/cfg.md) page.
Train YOLOv8n on the COCO8 dataset for 100 [epochs](https://www.ultralytics.com/glossary/epoch) at image size 640. For a full list of available arguments see the [Configuration](../usage/cfg.md) page.
!!! example
@ -83,7 +83,7 @@ YOLO detection dataset format can be found in detail in the [Dataset Guide](../d
## Val
Validate trained YOLOv8n model accuracy on the COCO8 dataset. No arguments are needed as the `model` retains its training `data` and arguments as model attributes.
Validate trained YOLOv8n model [accuracy](https://www.ultralytics.com/glossary/accuracy) on the COCO8 dataset. No arguments are needed as the `model` retains its training `data` and arguments as model attributes.
!!! example