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

Update to lowercase MkDocs admonitions (#15990)

Browse source 
Co-authored-by: UltralyticsAssistant <web@ultralytics.com>
Co-authored-by: Glenn Jocher <glenn.jocher@ultralytics.com>
This commit is contained in:
MatthewNoyce 2024-09-06 16:33:26 +01:00 • committed by GitHub
parent ce24c7273e
commit c2b647a768
No known key found for this signature in database
GPG key ID: B5690EEEBB952194
133 changed files with 529 additions and 521 deletions
Download patch file Download diff file Expand all files Collapse all files

10
docs/en/models/yolov8.md
View file

@ -48,7 +48,7 @@ This table provides an overview of the YOLOv8 model variants, highlighting their
## Performance Metrics
!!! Performance
!!! performance
=== "Detection (COCO)"
@ -129,7 +129,7 @@ This example provides simple YOLOv8 training and inference examples. For full do
Note the below example is for YOLOv8 [Detect](../tasks/detect.md) models for object detection. For additional supported tasks see the [Segment](../tasks/segment.md), [Classify](../tasks/classify.md), [OBB](../tasks/obb.md) docs and [Pose](../tasks/pose.md) docs.
!!! Example
!!! example
=== "Python"
@ -167,7 +167,7 @@ Note the below example is for YOLOv8 [Detect](../tasks/detect.md) models for obj
If you use the YOLOv8 model or any other software from this repository in your work, please cite it using the following format:
!!! Quote ""
!!! quote ""
=== "BibTeX"
@ -203,7 +203,7 @@ YOLOv8 models achieve state-of-the-art performance across various benchmarking d
Training a YOLOv8 model can be done using either Python or CLI. Below are examples for training a model using a COCO-pretrained YOLOv8 model on the COCO8 dataset for 100 epochs:
!!! Example
!!! example
=== "Python"
@ -229,7 +229,7 @@ For further details, visit the [Training](../modes/train.md) documentation.
Yes, YOLOv8 models can be benchmarked for performance in terms of speed and accuracy across various export formats. You can use PyTorch, ONNX, TensorRT, and more for benchmarking. Below are example commands for benchmarking using Python and CLI:
!!! Example
!!! example
=== "Python"