HongChuang/ultralytics-ascend
1
0
Fork 0
Code Issues Pull requests Projects Releases Packages Wiki Activity Actions 3

Add Docs glossary links (#16448)

Browse source 
Signed-off-by: UltralyticsAssistant <web@ultralytics.com>
Co-authored-by: UltralyticsAssistant <web@ultralytics.com>
This commit is contained in:
Glenn Jocher 2024-09-23 23:48:46 +02:00 • committed by GitHub
parent 8b8c25f216
commit 443fbce194
No known key found for this signature in database
GPG key ID: B5690EEEBB952194
193 changed files with 1124 additions and 1124 deletions
Download patch file Download diff file Expand all files Collapse all files

6
docs/en/usage/simple-utilities.md
View file

@ -31,7 +31,7 @@ The `ultralytics` package comes with a myriad of utilities that can support, enh
### Auto Labeling / Annotations
Dataset annotation is a very resource intensive and time-consuming process. If you have a YOLO object detection model trained on a reasonable amount of data, you can use it and [SAM](../models/sam.md) to auto-annotate additional data (segmentation format).
Dataset annotation is a very resource intensive and time-consuming process. If you have a YOLO [object detection](https://www.ultralytics.com/glossary/object-detection) model trained on a reasonable amount of data, you can use it and [SAM](../models/sam.md) to auto-annotate additional data (segmentation format).
```{ .py .annotate }
from ultralytics.data.annotator import auto_annotate
@ -86,7 +86,7 @@ convert_coco( # (1)!
For additional information about the `convert_coco` function, [visit the reference page](../reference/data/converter.md#ultralytics.data.converter.convert_coco)
### Get Bounding Box Dimensions
### Get [Bounding Box](https://www.ultralytics.com/glossary/bounding-box) Dimensions
```{.py .annotate }
from ultralytics.utils.plotting import Annotator
@ -585,7 +585,7 @@ make_divisible(7, 2)
## FAQ
### What utilities are included in the Ultralytics package to enhance machine learning workflows?
### What utilities are included in the Ultralytics package to enhance [machine learning](https://www.ultralytics.com/glossary/machine-learning-ml) workflows?
The Ultralytics package includes a variety of utilities designed to streamline and optimize machine learning workflows. Key utilities include [auto-annotation](../reference/data/annotator.md#ultralytics.data.annotator.auto_annotate) for labeling datasets, converting COCO to YOLO format with [convert_coco](../reference/data/converter.md#ultralytics.data.converter.convert_coco), compressing images, and dataset auto-splitting. These tools aim to reduce manual effort, ensure consistency, and enhance data processing efficiency.