diff --git a/docs/en/hub/models.md b/docs/en/hub/models.md index d5739610..3a0d5e36 100644 --- a/docs/en/hub/models.md +++ b/docs/en/hub/models.md @@ -130,6 +130,16 @@ When the training starts, you can click **Done** and monitor the training progre #### c. Bring your own agent +

+ +
+ Watch: Bring your Own Agent model training using Ultralytics HUB +

+ 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) diff --git a/docs/en/models/rtdetr.md b/docs/en/models/rtdetr.md index b70f0b58..e93d036f 100644 --- a/docs/en/models/rtdetr.md +++ b/docs/en/models/rtdetr.md @@ -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.