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Add https://youtu.be/yXQPLMrNX2s to docs (#16626)

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11
docs/en/models/mobile-sam.md
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@ -12,6 +12,17 @@ The MobileSAM paper is now available on [arXiv](https://arxiv.org/pdf/2306.14289
A demonstration of MobileSAM running on a CPU can be accessed at this [demo link](https://huggingface.co/spaces/dhkim2810/MobileSAM). The performance on a Mac i5 CPU takes approximately 3 seconds. On the Hugging Face demo, the interface and lower-performance CPUs contribute to a slower response, but it continues to function effectively.
<p align="center">
<br>
<iframe loading="lazy" width="720" height="405" src="https://www.youtube.com/embed/yXQPLMrNX2s"
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 Run Inference with MobileSAM using Ultralytics | Step-by-Step Guide 🎉
</p>
MobileSAM is implemented in various projects including [Grounding-SAM](https://github.com/IDEA-Research/Grounded-Segment-Anything), [AnyLabeling](https://github.com/vietanhdev/anylabeling), and [Segment Anything in 3D](https://github.com/Jumpat/SegmentAnythingin3D).
MobileSAM is trained on a single GPU with a 100k dataset (1% of the original images) in less than a day. The code for this training will be made available in the future.

11
docs/en/models/sam-2.md
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@ -12,6 +12,17 @@ SAM 2, the successor to Meta's [Segment Anything Model (SAM)](sam.md), is a cutt
## Key Features
<p align="center">
<br>
<iframe loading="lazy" width="720" height="405" src="https://www.youtube.com/embed/yXQPLMrNX2s"
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 Run Inference with Meta's SAM2 using Ultralytics | Step-by-Step Guide 🎉
</p>
### Unified Model Architecture
SAM 2 combines the capabilities of image and video segmentation in a single model. This unification simplifies deployment and allows for consistent performance across different media types. It leverages a flexible prompt-based interface, enabling users to specify objects of interest through various prompt types, such as points, bounding boxes, or masks.