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

Fix docs links (#7096)

Browse source 
Signed-off-by: Glenn Jocher <glenn.jocher@ultralytics.com>
Co-authored-by: Muhammad Rizwan Munawar <chr043416@gmail.com>
This commit is contained in:
Glenn Jocher 2023-12-21 00:44:55 +01:00 • committed by GitHub
parent 6cbe736bfd
commit f955978dc4
No known key found for this signature in database
GPG key ID: 4AEE18F83AFDEB23
13 changed files with 12 additions and 23 deletions
Download patch file Download diff file Expand all files Collapse all files

11
docs/en/guides/vision-eye.md
View file

@ -10,17 +10,6 @@ keywords: Ultralytics, YOLOv8, Object Detection, Object Tracking, IDetection, Vi
[Ultralytics YOLOv8](https://github.com/ultralytics/ultralytics/) VisionEye offers the capability for computers to identify and pinpoint objects, simulating the observational precision of the human eye. This functionality enables computers to discern and focus on specific objects, much like the way the human eye observes details from a particular viewpoint.
<p align="center">
<br>
<iframe width="720" height="405" src="https://www.youtube.com/embed/in6xF7KgF7Q"
title="YouTube video player" frameborder="0"
allow="accelerometer; autoplay; clipboard-write; encrypted-media; gyroscope; picture-in-picture; web-share"
allowfullscreen>
</iframe>
<br>
<strong>Watch:</strong> VisionEye Mapping using Ultralytics YOLOv8
</p>
## Samples
| VisionEye View | VisionEye View With Object Tracking |

2
docs/en/models/yolov4.md
View file

@ -68,4 +68,4 @@ We would like to acknowledge the YOLOv4 authors for their significant contributi
}
```
The original YOLOv4 paper can be found on [arXiv](https://arxiv.org/pdf/2004.10934.pdf). The authors have made their work publicly available, and the codebase can be accessed on [GitHub](https://github.com/AlexeyAB/darknet). We appreciate their efforts in advancing the field and making their work accessible to the broader community.
The original YOLOv4 paper can be found on [arXiv](https://arxiv.org/abs/2004.10934). The authors have made their work publicly available, and the codebase can be accessed on [GitHub](https://github.com/AlexeyAB/darknet). We appreciate their efforts in advancing the field and making their work accessible to the broader community.

2
docs/en/yolov5/environments/google_cloud_quickstart_tutorial.md
View file

@ -23,7 +23,7 @@ Let’s begin by creating a virtual machine that’s tuned for deep learning:
5. Allocate a 300 GB SSD Persistent Disk to ensure you don't bottleneck on I/O operations.
6. Hit 'Deploy' and let GCP do its magic in provisioning your custom Deep Learning VM.
This VM comes loaded with a treasure trove of preinstalled tools and frameworks, including the [Anaconda](https://docs.anaconda.com/anaconda/packages/pkg-docs/) Python distribution, which conveniently bundles all the necessary dependencies for YOLOv5.
This VM comes loaded with a treasure trove of preinstalled tools and frameworks, including the [Anaconda](https://www.anaconda.com/) Python distribution, which conveniently bundles all the necessary dependencies for YOLOv5.
![GCP Marketplace illustration of setting up a Deep Learning VM](https://user-images.githubusercontent.com/26833433/105811495-95863880-5f61-11eb-841d-c2f2a5aa0ffe.png)