Improved Docs models Usage examples (#4214)

docs/models/rtdetr.md

@@ -15,7 +15,7 @@ Real-Time Detection Transformer (RT-DETR), developed by Baidu, is a cutting-edge
 
 ### Key Features
 
-- **Efficient Hybrid Encoder:** Baidu's RT-DETR uses an efficient hybrid encoder that processes multi-scale features by decoupling intra-scale interaction and cross-scale fusion. This unique Vision Transformers-based design reduces computational costs and allows for real-time object detection.
+- **Efficient Hybrid Encoder:** Baidu's RT-DETR uses an efficient hybrid encoder that processes multiscale features by decoupling intra-scale interaction and cross-scale fusion. This unique Vision Transformers-based design reduces computational costs and allows for real-time object detection.
 - **IoU-aware Query Selection:** Baidu's RT-DETR improves object query initialization by utilizing IoU-aware query selection. This allows the model to focus on the most relevant objects in the scene, enhancing the detection accuracy.
 - **Adaptable Inference Speed:** Baidu's RT-DETR supports flexible adjustments of inference speed by using different decoder layers without the need for retraining. This adaptability facilitates practical application in various real-time object detection scenarios.
 
@@ -28,16 +28,39 @@ The Ultralytics Python API provides pre-trained PaddlePaddle RT-DETR models with
 
 ## Usage
 
-### Python API
+You can use RT-DETR for object detection tasks using the `ultralytics` pip package. The following is a sample code snippet showing how to use RT-DETR models for training and inference:
 
-```python
-from ultralytics import RTDETR
+!!! example ""
 
-model = RTDETR("rtdetr-l.pt")
-model.info()  # display model information
-model.train(data="coco8.yaml")   # train
-model.predict("path/to/image.jpg")  # predict
-```
+    This example provides simple inference code for RT-DETR. For more options including handling inference results see [Predict](../modes/predict.md) mode. For using RT-DETR with additional modes see [Train](../modes/train.md), [Val](../modes/val.md) and [Export](../modes/export.md).
+
+    === "Python"
+
+        ```python
+        from ultralytics import RTDETR
+
+        # Load a COCO-pretrained RT-DETR-l model
+        model = RTDETR('rtdetr-l.pt')
+
+        # Display model information (optional)
+        model.info()
+
+        # Train the model on the COCO8 example dataset for 100 epochs
+        results model.train(data='coco8.yaml', epochs=100, imgsz=640)
+
+        # Run inference with the RT-DETR-l model on the 'bus.jpg' image
+        results = model('path/to/bus.jpg')
+        ```
+
+    === "CLI"
+
+        ```bash        
+        # Load a COCO-pretrained RT-DETR-l model and train it on the COCO8 example dataset for 100 epochs
+        yolo train model=rtdetr-l.pt data=coco8.yaml epochs=100 imgsz=640
+
+        # Load a COCO-pretrained RT-DETR-l model and run inference on the 'bus.jpg' image
+        yolo predict model=rtdetr-l.pt source=path/to/bus.jpg
+        ```
 
 ### Supported Tasks
 
@@ -54,20 +77,24 @@ model.predict("path/to/image.jpg")  # predict
 | Validation | :heavy_check_mark: |
 | Training   | :heavy_check_mark: |
 
-# Citations and Acknowledgements
+## Citations and Acknowledgements
 
 If you use Baidu's RT-DETR in your research or development work, please cite the [original paper](https://arxiv.org/abs/2304.08069):
 
-```bibtex
-@misc{lv2023detrs,
-      title={DETRs Beat YOLOs on Real-time Object Detection},
-      author={Wenyu Lv and Shangliang Xu and Yian Zhao and Guanzhong Wang and Jinman Wei and Cheng Cui and Yuning Du and Qingqing Dang and Yi Liu},
-      year={2023},
-      eprint={2304.08069},
-      archivePrefix={arXiv},
-      primaryClass={cs.CV}
-}
-```
+!!! note ""
+
+    === "BibTeX"
+
+        ```bibtex
+        @misc{lv2023detrs,
+              title={DETRs Beat YOLOs on Real-time Object Detection},
+              author={Wenyu Lv and Shangliang Xu and Yian Zhao and Guanzhong Wang and Jinman Wei and Cheng Cui and Yuning Du and Qingqing Dang and Yi Liu},
+              year={2023},
+              eprint={2304.08069},
+              archivePrefix={arXiv},
+              primaryClass={cs.CV}
+        }
+        ```
 
 We would like to acknowledge Baidu and the [PaddlePaddle](https://github.com/PaddlePaddle/PaddleDetection) team for creating and maintaining this valuable resource for the computer vision community. Their contribution to the field with the development of the Vision Transformers-based real-time object detector, RT-DETR, is greatly appreciated.