Add Ultralytics tasks and YOLO-NAS models (ultralytics#2735)

Co-authored-by: pre-commit-ci[bot] <66853113+pre-commit-ci[bot]@users.noreply.github.com>

README.md

@@ -102,9 +102,11 @@ path = model.export(format="onnx")  # export the model to ONNX format
 
 ## <div align="center">Models</div>
 
-All YOLOv8 pretrained models are available here. Detect, Segment and Pose models are pretrained on the [COCO](https://github.com/ultralytics/ultralytics/blob/main/ultralytics/datasets/coco.yaml) dataset, while Classify models are pretrained on the [ImageNet](https://github.com/ultralytics/ultralytics/blob/main/ultralytics/datasets/ImageNet.yaml) dataset.
+YOLOv8 [Detect](https://docs.ultralytics.com/tasks/detect), [Segment](https://docs.ultralytics.com/tasks/segment) and [Pose](https://docs.ultralytics.com/tasks/pose) models pretrained on the [COCO](https://docs.ultralytics.com/datasets/detect/coco) dataset are available here, as well as YOLOv8 [Classify](https://docs.ultralytics.com/modes/classify) models pretrained on the [ImageNet](https://docs.ultralytics.com/datasets/classify/imagenet) dataset. [Track](https://docs.ultralytics.com/modes/track) mode is available for all Detect, Segment and Pose models.
 
-[Models](https://github.com/ultralytics/ultralytics/tree/main/ultralytics/models) download automatically from the latest Ultralytics [release](https://github.com/ultralytics/assets/releases) on first use.
+<img width="1024" src="https://raw.githubusercontent.com/ultralytics/assets/tasks/im/banner-tasks.png">
+
+All [Models](https://github.com/ultralytics/ultralytics/tree/main/ultralytics/models) download automatically from the latest Ultralytics [release](https://github.com/ultralytics/assets/releases) on first use.
 
 <details open><summary>Detection</summary>
 

README.zh-CN.md

@@ -102,9 +102,11 @@ success = model.export(format="onnx")  # 将模型导出为 ONNX 格式
 
 ## <div align="center">模型</div>
 
-所有的 YOLOv8 预训练模型都可以在此找到。检测、分割和姿态模型在 [COCO](https://github.com/ultralytics/ultralytics/blob/main/ultralytics/datasets/coco.yaml) 数据集上进行预训练，而分类模型在 [ImageNet](https://github.com/ultralytics/ultralytics/blob/main/ultralytics/datasets/ImageNet.yaml) 数据集上进行预训练。
+在[COCO](https://docs.ultralytics.com/datasets/detect/coco)数据集上预训练的YOLOv8 [检测](https://docs.ultralytics.com/tasks/detect)，[分割](https://docs.ultralytics.com/tasks/segment)和[姿态](https://docs.ultralytics.com/tasks/pose)模型可以在这里找到，以及在[ImageNet](https://docs.ultralytics.com/datasets/classify/imagenet)数据集上预训练的YOLOv8 [分类](https://docs.ultralytics.com/modes/classify)模型。所有的检测，分割和姿态模型都支持[追踪](https://docs.ultralytics.com/modes/track)模式。
 
-在首次使用时，[模型](https://github.com/ultralytics/ultralytics/tree/main/ultralytics/models) 会自动从最新的 Ultralytics [发布版本](https://github.com/ultralytics/assets/releases)中下载。
+<img width="1024" src="https://raw.githubusercontent.com/ultralytics/assets/tasks/im/banner-tasks.png">
+
+所有[模型](https://github.com/ultralytics/ultralytics/tree/main/ultralytics/models)在首次使用时会自动从最新的Ultralytics [发布版本](https://github.com/ultralytics/assets/releases)下载。
 
 <details open><summary>检测</summary>
 

docs/models/index.md

@@ -12,9 +12,10 @@ In this documentation, we provide information on four major models:
 1. [YOLOv3](./yolov3.md): The third iteration of the YOLO model family, known for its efficient real-time object detection capabilities.
 2. [YOLOv5](./yolov5.md): An improved version of the YOLO architecture, offering better performance and speed tradeoffs compared to previous versions.
 3. [YOLOv6](./yolov6.md): Released by [Meituan](https://about.meituan.com/) in 2022 and is in use in many of the company's autonomous delivery robots.
-3. [YOLOv8](./yolov8.md): The latest version of the YOLO family, featuring enhanced capabilities such as instance segmentation, pose/keypoints estimation, and classification.
-4. [Segment Anything Model (SAM)](./sam.md): Meta's Segment Anything Model (SAM).
-5. [Realtime Detection Transformers (RT-DETR)](./rtdetr.md): Baidu's RT-DETR model.
+4. [YOLOv8](./yolov8.md): The latest version of the YOLO family, featuring enhanced capabilities such as instance segmentation, pose/keypoints estimation, and classification.
+5. [Segment Anything Model (SAM)](./sam.md): Meta's Segment Anything Model (SAM).
+6. [YOLO-NAS](./yolo-nas.md): YOLO Neural Architecture Search (NAS) Models.
+7. [Realtime Detection Transformers (RT-DETR)](./rtdetr.md): Baidu's PaddlePaddle Realtime Detection Transformer (RT-DETR) models.
 
 You can use these models directly in the Command Line Interface (CLI) or in a Python environment. Below are examples of how to use the models with CLI and Python:
 
@@ -35,4 +36,4 @@ model.info()  # display model information
 model.train(data="coco128.yaml", epochs=100)  # train the model
 ```
 
-For more details on each model, their supported tasks, modes, and performance, please visit their respective documentation pages linked above.
+For more details on each model, their supported tasks, modes, and performance, please visit their respective documentation pages linked above.

docs/models/rtdetr.md

@@ -1,29 +1,26 @@
 ---
 comments: true
-description: Explore RT-DETR, a high-performance real-time object detector. Learn how to use pre-trained models with Ultralytics Python API for various tasks.
+description: Dive into Baidu's RT-DETR, a revolutionary real-time object detection model built on the foundation of Vision Transformers (ViT). Learn how to use pre-trained PaddlePaddle RT-DETR models with the Ultralytics Python API for various tasks.
 ---
 
-# RT-DETR
+# Baidu's RT-DETR: A Vision Transformer-Based Real-Time Object Detector
 
 ## Overview
 
-Real-Time Detection Transformer (RT-DETR) is an end-to-end object detector that provides real-time performance while maintaining high accuracy. It efficiently processes multi-scale features by decoupling intra-scale interaction and cross-scale fusion, and supports flexible adjustment of inference speed using different decoder layers without retraining. RT-DETR outperforms many real-time object detectors on accelerated backends like CUDA with TensorRT.
+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.
 
 ![Model example image](https://user-images.githubusercontent.com/26833433/238963168-90e8483f-90aa-4eb6-a5e1-0d408b23dd33.png)
-**Overview of RT-DETR.** Model architecture diagram showing 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.pdf)).
+**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.pdf)).
 
 ### Key Features
 
-- **Efficient Hybrid Encoder:** RT-DETR uses an efficient hybrid encoder that processes multi-scale features by decoupling intra-scale interaction and cross-scale fusion. This design reduces computational costs and allows for real-time object detection.
-- **IoU-aware Query Selection:** 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.
-- **Adaptable Inference Speed:** 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.
+- **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.
+- **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.
 
 ## Pre-trained Models
 
-Ultralytics RT-DETR provides several pre-trained models with different scales:
+The Ultralytics Python API provides pre-trained PaddlePaddle RT-DETR models with different scales:
 
 - RT-DETR-L: 53.0% AP on COCO val2017, 114 FPS on T4 GPU
 - RT-DETR-X: 54.8% AP on COCO val2017, 74 FPS on T4 GPU
@@ -57,7 +54,7 @@ model.predict("path/to/image.jpg")  # predict
 
 # Citations and Acknowledgements
 
-If you use RT-DETR in your research or development work, please cite the [original paper](https://arxiv.org/abs/2304.08069):
+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,
@@ -70,4 +67,6 @@ If you use RT-DETR in your research or development work, please cite the [origin
 }
 ```
 
-We would like to acknowledge Baidu's [PaddlePaddle](https://github.com/PaddlePaddle/PaddleDetection) team for creating and maintaining this valuable resource for the computer vision community.
+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.
+
+*Keywords: RT-DETR, Transformer, ViT, Vision Transformers, Baidu RT-DETR, PaddlePaddle, Paddle Paddle RT-DETR, real-time object detection, Vision Transformers-based object detection, pre-trained PaddlePaddle RT-DETR models, Baidu's RT-DETR usage, Ultralytics Python API*