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Add https://youtu.be/leOPZhE0ckg to docs and refresh outdated embeds (#22408)

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4
docs/en/guides/distance-calculation.md
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@ -12,13 +12,13 @@ Measuring the gap between two objects is known as distance calculation within a
<p align="center">
<br>
<iframe loading="lazy" width="720" height="405" src="https://www.youtube.com/embed/LE8am1QoVn4"
<iframe loading="lazy" width="720" height="405" src="https://www.youtube.com/embed/Oe0vmsvnY74"
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> Distance Calculation using Ultralytics YOLO11
<strong>Watch:</strong> How to estimate distance between detected objects with Ultralytics YOLO in Pixels 🚀
</p>
## Visuals

4
docs/en/models/rtdetr.md
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@ -12,13 +12,13 @@ Real-Time Detection Transformer (RT-DETR), developed by Baidu, is a cutting-edge
<p align="center">
<br>
<iframe loading="lazy" width="720" height="405" src="https://www.youtube.com/embed/SArFQs6CHwk"
<iframe loading="lazy" width="720" height="405" src="https://www.youtube.com/embed/i70ecEGB1ro"
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> Real-Time Detection Transformer (RT-DETR)
<strong>Watch:</strong> How to Use Baidu's RT-DETR for Object Detection | Inference and Benchmarking with Ultralytics 🚀
</p>
![Model example image](https://github.com/ultralytics/docs/releases/download/0/baidu-rtdetr-model-overview.avif) **Overview of Baidu's RT-DETR.** The RT-DETR model architecture diagram shows the last three stages of the backbone {S3, S4, S5} as the input to the encoder. The efficient hybrid encoder transforms multiscale features into a sequence of image features through intrascale feature interaction (AIFI) and cross-scale feature-fusion module (CCFM). The IoU-aware query selection is employed to select a fixed number of image features to serve as initial object queries for the decoder. Finally, the decoder with auxiliary prediction heads iteratively optimizes object queries to generate boxes and confidence scores ([source](https://arxiv.org/pdf/2304.08069)).

11
docs/en/models/yolo-world.md
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@ -79,6 +79,17 @@ This section details the models available with their specific pre-trained weight
The YOLO-World models are easy to integrate into your Python applications. Ultralytics provides user-friendly [Python API](../usage/python.md) and [CLI commands](../usage/cli.md) to streamline development.
<p align="center">
<br>
<iframe loading="lazy" width="720" height="405" src="https://www.youtube.com/embed/sWEm3dIGKU8"
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> YOLO-World Model Usage examples with Ultralytics | Open Vocab, Prompt-Free & others 🚀
</p>
### Train Usage
!!! tip

11
docs/en/models/yoloe.md
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@ -79,6 +79,17 @@ The YOLOE models are easy to integrate into your Python applications. Ultralytic
You can fine-tune any [pretrained YOLOE model](#textvisual-prompt-models) on your custom YOLO dataset for both detection and instance segmentation tasks.
<p align="center">
<br>
<iframe loading="lazy" width="720" height="405" src="https://www.youtube.com/embed/vnn90bEyk0w"
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 Train YOLOE on Car Parts Segmentation Dataset | Open-Vocabulary Model, Prediction & Export 🚀
</p>
!!! example
=== "Fine-Tuning"

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docs/en/modes/track.md
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@ -202,6 +202,17 @@ Once exported, you can point to the TensorRT model path in your tracker config,
## Python Examples
<p align="center">
<br>
<iframe loading="lazy" width="720" height="405" src="https://www.youtube.com/embed/leOPZhE0ckg"
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 Build Interactive Object Tracking with Ultralytics YOLO | Click to Crop & Display ⚡
</p>
### Persisting Tracks Loop
Here is a Python script using [OpenCV](https://www.ultralytics.com/glossary/opencv) (`cv2`) and YOLO11 to run object tracking on video frames. This script still assumes you have already installed the necessary packages (`opencv-python` and `ultralytics`). The `persist=True` argument tells the tracker that the current image or frame is the next in a sequence and to expect tracks from the previous image in the current image.