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52 lines
2.4 KiB
52 lines
2.4 KiB
1 year ago
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---
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comments: true
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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.
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---
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# RT-DETR
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## Overview
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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.
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### Key Features
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- **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.
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- **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.
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- **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.
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## Pre-trained Models
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Ultralytics RT-DETR provides several pre-trained models with different scales:
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- RT-DETR-L: 53.0% AP on COCO val2017, 114 FPS on T4 GPU
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- RT-DETR-X: 54.8% AP on COCO val2017, 74 FPS on T4 GPU
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## Usage
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### Python API
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```python
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from ultralytics import RTDETR
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model = RTDETR("rtdetr-l.pt")
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model.info() # display model information
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model.predict("path/to/image.jpg") # predict
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```
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### Supported Tasks
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| Model Type | Pre-trained Weights | Tasks Supported |
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|---------------------|---------------------|------------------|
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| RT-DETR Large | `rtdetr-l.pt` | Object Detection |
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| RT-DETR Extra-Large | `rtdetr-x.pt` | Object Detection |
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### Supported Modes
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| Mode | Supported |
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|------------|--------------------|
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| Inference | :heavy_check_mark: |
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| Validation | :heavy_check_mark: |
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| Training | :x: (Coming soon) |
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For more information about the RT-DETR model, please refer to the [original paper](https://arxiv.org/abs/2304.08069) and the [PaddleDetection repository](https://github.com/PaddlePaddle/PaddleDetection).
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