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

Add https://youtu.be/K69DUpSBNdA to docs (#18507)

Browse source
This commit is contained in:
Muhammad Rizwan Munawar 2025-01-03 17:34:24 +05:00 • committed by GitHub
parent 7e4416f64f
commit ed9fb82764
No known key found for this signature in database
GPG key ID: B5690EEEBB952194
2 changed files with 20 additions and 22 deletions
Download patch file Download diff file Expand all files Collapse all files

18
docs/en/guides/object-counting.md
View file

@ -10,28 +10,16 @@ keywords: object counting, YOLO11, Ultralytics, real-time object detection, AI,
Object counting with [Ultralytics YOLO11](https://github.com/ultralytics/ultralytics/) involves accurate identification and counting of specific objects in videos and camera streams. YOLO11 excels in real-time applications, providing efficient and precise object counting for various scenarios like crowd analysis and surveillance, thanks to its state-of-the-art algorithms and [deep learning](https://www.ultralytics.com/glossary/deep-learning-dl) capabilities.
<table>
<tr>
<td align="center">
<iframe loading="lazy" width="720" height="405" src="https://www.youtube.com/embed/Ag2e-5_NpS0"
title="YouTube video player" frameborder="0"
allow="accelerometer; autoplay; clipboard-write; encrypted-media; gyroscope; picture-in-picture; web-share"
allowfullscreen>
</iframe>
<p align="center">
<br>
<strong>Watch:</strong> Object Counting using Ultralytics YOLOv8
</td>
<td align="center">
<iframe loading="lazy" width="720" height="405" src="https://www.youtube.com/embed/Fj9TStNBVoY"
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> Class-wise Object Counting using Ultralytics YOLO11
</td>
</tr>
</table>
<strong>Watch:</strong> Class-wise Object Counting using Ultralytics YOLOv8
</p>
## Advantages of Object Counting?

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

@ -221,6 +221,16 @@ Furthermore, you can preview your model in real-time directly on your [iOS](http
After you [train a model](#train-model), you can export it to 13 different formats, including ONNX, OpenVINO, CoreML, [TensorFlow](https://www.ultralytics.com/glossary/tensorflow), Paddle and many others.
<p align="center">
<iframe loading="lazy" width="720" height="405" src="https://www.youtube.com/embed/K69DUpSBNdA"
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> How to Export the Ultralytics YOLO11 to ONNX, OpenVINO and Other Formats using Ultralytics HUB 🚀
</p>
![Ultralytics HUB screenshot of the Deploy tab inside the Model page with an arrow pointing to the Export card and all formats exported](https://github.com/ultralytics/docs/releases/download/0/ultralytics-hub-deploy-export-formats.avif)
??? tip