退购1.1定位算法

docs/models/rtdetr.md

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+---
+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.
+---
+
+# RT-DETR
+
+## 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.
+
+### 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.
+
+## Pre-trained Models
+
+Ultralytics RT-DETR provides several pre-trained 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
+
+## Usage
+
+### Python API
+
+```python
+from ultralytics import RTDETR
+
+model = RTDETR("rtdetr-l.pt")
+model.info()  # display model information
+model.predict("path/to/image.jpg")  # predict
+```
+
+### Supported Tasks
+
+| Model Type          | Pre-trained Weights | Tasks Supported  |
+|---------------------|---------------------|------------------|
+| RT-DETR Large       | `rtdetr-l.pt`       | Object Detection |
+| RT-DETR Extra-Large | `rtdetr-x.pt`       | Object Detection |
+
+### Supported Modes
+
+| Mode       | Supported          |
+|------------|--------------------|
+| Inference  | :heavy_check_mark: |
+| Validation | :heavy_check_mark: |
+| Training   | :x: (Coming soon)  |
+
+# 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):
+
+```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's [PaddlePaddle]((https://github.com/PaddlePaddle/PaddleDetection)) team for creating and maintaining this valuable resource for the computer vision community.