You can not select more than 25 topics
Topics must start with a letter or number, can include dashes ('-') and can be up to 35 characters long.
52 lines
2.4 KiB
52 lines
2.4 KiB
2 years ago
|
---
|
||
|
|
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) |
|
||
|
|
|
||
|
|
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).
|