diff --git a/docs/en/guides/object-counting.md b/docs/en/guides/object-counting.md index 59a50cb5..30b6a755 100644 --- a/docs/en/guides/object-counting.md +++ b/docs/en/guides/object-counting.md @@ -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. - - - - - -
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- Watch: Object Counting using Ultralytics YOLOv8 - 		- -
- Watch: Class-wise Object Counting using Ultralytics YOLO11 -
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+ Watch: Class-wise Object Counting using Ultralytics YOLOv8 +

## Advantages of Object Counting? diff --git a/docs/en/hub/models.md b/docs/en/hub/models.md index 45cc7881..c123f25b 100644 --- a/docs/en/hub/models.md +++ b/docs/en/hub/models.md @@ -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. +

+ +
+ Watch: How to Export the Ultralytics YOLO11 to ONNX, OpenVINO and Other Formats using Ultralytics HUB 🚀 +

+ ![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