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

6
docs/en/models/rtdetr.md
View file

@ -40,7 +40,7 @@ The Ultralytics Python API provides pre-trained PaddlePaddle RT-DETR models with
This example provides simple RT-DETR training and inference examples. For full documentation on these and other [modes](../modes/index.md) see the [Predict](../modes/predict.md), [Train](../modes/train.md), [Val](../modes/val.md) and [Export](../modes/export.md) docs pages.
!!! Example
!!! example
=== "Python"
@ -83,7 +83,7 @@ This table presents the model types, the specific pre-trained weights, the tasks
If you use Baidu's RT-DETR in your research or development work, please cite the [original paper](https://arxiv.org/abs/2304.08069):
!!! Quote ""
!!! quote ""
=== "BibTeX"
@ -110,7 +110,7 @@ Baidu's RT-DETR (Real-Time Detection Transformer) is an advanced real-time objec
You can leverage Ultralytics Python API to use pre-trained PaddlePaddle RT-DETR models. For instance, to load an RT-DETR-l model pre-trained on COCO val2017 and achieve high FPS on T4 GPU, you can utilize the following example:
!!! Example
!!! example
=== "Python"