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Add https://youtu.be/S_J-Dyw15i0 to docs (#13099)

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Muhammad Rizwan Munawar 2024-05-24 16:01:29 +05:00 • committed by GitHub
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10
docs/en/hub/models.md
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@ -130,6 +130,16 @@ When the training starts, you can click **Done** and monitor the training progre
#### c. Bring your own agent
<p align="center">
<iframe loading="lazy" width="720" height="405" src="https://www.youtube.com/embed/S_J-Dyw15i0"
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> Bring your Own Agent model training using Ultralytics HUB
</p>
To start training your model using your own agent, follow the instructions shown in the [Ultralytics HUB](https://bit.ly/ultralytics_hub) **Train Model** dialog.
![Ultralytics HUB screenshot of the Train Model dialog with arrows pointing to instructions](https://raw.githubusercontent.com/ultralytics/assets/main/docs/hub/models/hub_train_model_12.jpg)

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docs/en/models/rtdetr.md
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@ -7,8 +7,7 @@ keywords: RT-DETR, Baidu, Vision Transformers, object detection, real-time perfo
# Baidu's RT-DETR: A Vision Transformer-Based Real-Time Object Detector
## Overview
Real-Time Detection Transformer (RT-DETR), developed by Baidu, is a cutting-edge end-to-end object detector that provides real-time performance while maintaining high accuracy. It leverages the power of Vision Transformers (ViT) to efficiently process multiscale features by decoupling intra-scale interaction and cross-scale fusion. RT-DETR is highly adaptable, supporting flexible adjustment of inference speed using different decoder layers without retraining. The model excels on accelerated backends like CUDA with TensorRT, outperforming many other real-time object detectors.
Real-Time Detection Transformer (RT-DETR), developed by Baidu, is a cutting-edge end-to-end object detector that provides real-time performance while maintaining high accuracy. It is based on the idea of DETR (the NMS-free framework), meanwhile introducing conv-based backbone and an efficient hybrid encoder to gain real-time speed. RT-DETR efficiently processes multiscale features by decoupling intra-scale interaction and cross-scale fusion. The model is highly adaptable, supporting flexible adjustment of inference speed using different decoder layers without retraining. RT-DETR excels on accelerated backends like CUDA with TensorRT, outperforming many other real-time object detectors.
<p align="center">
<br>