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

Clean up Docs pages (#13370)

Browse source 
Co-authored-by: Glenn Jocher <glenn.jocher@ultralytics.com>
This commit is contained in:
Burhan 2024-06-06 04:17:41 -04:00 • committed by GitHub
parent 89108513c4
commit 22dec59b57
No known key found for this signature in database
GPG key ID: B5690EEEBB952194
2 changed files with 0 additions and 8 deletions
Download patch file Download diff file Expand all files Collapse all files

2
docs/en/datasets/segment/index.md
View file

@ -138,8 +138,6 @@ To auto-annotate your dataset using the Ultralytics framework, you can use the `
auto_annotate(data="path/to/images", det_model="yolov8x.pt", sam_model="sam_b.pt")
```
Certainly, here is the table updated with code snippets:
| Argument | Type | Description | Default |
|--------------|-------------------------|-------------------------------------------------------------------------------------------------------------|----------------|
| `data` | `str` | Path to a folder containing images to be annotated. | `None` |

6
docs/en/guides/conda-quickstart.md
View file

@ -102,12 +102,6 @@ sudo docker run -it --ipc=host --gpus all $t # all GPUs
sudo docker run -it --ipc=host --gpus '"device=2,3"' $t # specify GPUs
```
---
Certainly, you can include the following section in your Conda guide to inform users about speeding up installation using `libmamba`:
---
## Speeding Up Installation with Libmamba
If you're looking to [speed up the package installation](https://www.anaconda.com/blog/a-faster-conda-for-a-growing-community) process in Conda, you can opt to use `libmamba`, a fast, cross-platform, and dependency-aware package manager that serves as an alternative solver to Conda's default.