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Add Docs glossary links (#16448)

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docs/en/datasets/detect/globalwheat2020.md
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@ -24,7 +24,7 @@ The Global Wheat Head Dataset is organized into two main subsets:
## Applications
The Global Wheat Head Dataset is widely used for training and evaluating deep learning models in wheat head detection tasks. The dataset's diverse set of images, capturing a wide range of appearances, environments, and conditions, make it a valuable resource for researchers and practitioners in the field of plant phenotyping and crop management.
The Global Wheat Head Dataset is widely used for training and evaluating [deep learning](https://www.ultralytics.com/glossary/deep-learning-dl) models in wheat head detection tasks. The dataset's diverse set of images, capturing a wide range of appearances, environments, and conditions, make it a valuable resource for researchers and practitioners in the field of plant phenotyping and crop management.
## Dataset YAML
@ -38,7 +38,7 @@ A YAML (Yet Another Markup Language) file is used to define the dataset configur
## Usage
To train a YOLOv8n model on the Global Wheat Head Dataset for 100 epochs with an image size of 640, you can use the following code snippets. For a comprehensive list of available arguments, refer to the model [Training](../../modes/train.md) page.
To train a YOLOv8n model on the Global Wheat Head Dataset for 100 [epochs](https://www.ultralytics.com/glossary/epoch) with an image size of 640, you can use the following code snippets. For a comprehensive list of available arguments, refer to the model [Training](../../modes/train.md) page.
!!! example "Train Example"
@ -130,7 +130,7 @@ Key features of the Global Wheat Head Dataset include:
- Over 3,000 training images from Europe (France, UK, Switzerland) and North America (Canada).
- Approximately 1,000 test images from Australia, Japan, and China.
- High variability in wheat head appearances due to different growing environments.
- Detailed annotations with wheat head bounding boxes to aid object detection models.
- Detailed annotations with wheat head bounding boxes to aid [object detection](https://www.ultralytics.com/glossary/object-detection) models.
These features facilitate the development of robust models capable of generalization across multiple regions.