Add Docs glossary links (#16448)

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@ -6,7 +6,7 @@ keywords: Oriented Bounding Box, OBB Datasets, YOLO, Ultralytics, Object Detecti
# Oriented Bounding Box (OBB) Datasets Overview
Training a precise object detection model with oriented bounding boxes (OBB) requires a thorough dataset. This guide explains the various OBB dataset formats compatible with Ultralytics YOLO models, offering insights into their structure, application, and methods for format conversions.
Training a precise [object detection](https://www.ultralytics.com/glossary/object-detection) model with oriented bounding boxes (OBB) requires a thorough dataset. This guide explains the various OBB dataset formats compatible with Ultralytics YOLO models, offering insights into their structure, application, and methods for format conversions.
## Supported OBB Dataset Formats
@ -18,7 +18,7 @@ The YOLO OBB format designates bounding boxes by their four corner points with c
class_index x1 y1 x2 y2 x3 y3 x4 y4
```
Internally, YOLO processes losses and outputs in the `xywhr` format, which represents the bounding box's center point (xy), width, height, and rotation.
Internally, YOLO processes losses and outputs in the `xywhr` format, which represents the [bounding box](https://www.ultralytics.com/glossary/bounding-box)'s center point (xy), width, height, and rotation.
<p align="center"><img width="800" src="https://github.com/ultralytics/docs/releases/download/0/obb-format-examples.avif" alt="OBB format examples"></p>
@ -129,7 +129,7 @@ Training a YOLOv8 model with OBBs involves ensuring your dataset is in the YOLO
yolo obb train data=your_dataset.yaml model=yolov8n-obb.yaml epochs=100 imgsz=640
```
This ensures your model leverages the detailed OBB annotations for improved detection accuracy.
This ensures your model leverages the detailed OBB annotations for improved detection [accuracy](https://www.ultralytics.com/glossary/accuracy).
### What datasets are currently supported for OBB training in Ultralytics YOLO models?