---
library_name: transformers
license: apache-2.0
language:
- en
pipeline_tag: object-detection
---

# Model Card for Model ID

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## Model Details

### Model Description

The YOLO series has become the most popular framework for real-time object detection due to its reasonable trade-off between speed and accuracy. 
However, we observe that the speed and accuracy of YOLOs are negatively affected by the NMS. 
Recently, end-to-end Transformer-based detectors (DETRs) have provided an alternative to eliminating NMS. 
Nevertheless, the high computational cost limits their practicality and hinders them from fully exploiting the advantage of excluding NMS. 
In this paper, we propose the Real-Time DEtection TRansformer (RT-DETR), the first real-time end-to-end object detector to our best knowledge that addresses the above dilemma. 
We build RT-DETR in two steps, drawing on the advanced DETR: 
first we focus on maintaining accuracy while improving speed, followed by maintaining speed while improving accuracy. 
Specifically, we design an efficient hybrid encoder to expeditiously process multi-scale features by decoupling intra-scale interaction and cross-scale fusion to improve speed. 
Then, we propose the uncertainty-minimal query selection to provide high-quality initial queries to the decoder, thereby improving accuracy. 
In addition, RT-DETR supports flexible speed tuning by adjusting the number of decoder layers to adapt to various scenarios without retraining. 
Our RT-DETR-R50 / R101 achieves 53.1% / 54.3% AP on COCO and 108 / 74 FPS on T4 GPU, outperforming previously advanced YOLOs in both speed and accuracy. 
We also develop scaled RT-DETRs that outperform the lighter YOLO detectors (S and M models). 
Furthermore, RT-DETR-R50 outperforms DINO-R50 by 2.2% AP in accuracy and about 21 times in FPS. 
After pre-training with Objects365, RT-DETR-R50 / R101 achieves 55.3% / 56.2% AP. The project page: this [https URL](https://zhao-yian.github.io/RTDETR/).

![image/png](https://cdn-uploads.huggingface.co/production/uploads/6579e0eaa9e58aec614e9d97/WULSDLsCVs7RNEs9KB0Lr.png)

This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.

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- **Finetuned from model [optional]:** [More Information Needed]

### Model Sources [optional]

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- **Repository:** https://github.com/lyuwenyu/RT-DETR
- **Paper [optional]:** https://arxiv.org/abs/2304.08069
- **Demo [optional]:** [More Information Needed]

## Uses

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### Direct Use

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You can use the raw model for object detection. See the [model hub](https://huggingface.co/models?search=rtdetr) to look for all available RTDETR models.

### Downstream Use [optional]

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### Out-of-Scope Use

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## Bias, Risks, and Limitations

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[More Information Needed]

### Recommendations

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Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.

## How to Get Started with the Model

Use the code below to get started with the model.

```
import torch
import requests

from PIL import Image
from transformers import RTDetrForObjectDetection, RTDetrImageProcessor

url = 'http://images.cocodataset.org/val2017/000000039769.jpg' 
image = Image.open(requests.get(url, stream=True).raw)

image_processor = RTDetrImageProcessor.from_pretrained("PekingU/rtdetr_r50vd")
model = RTDetrForObjectDetection.from_pretrained("PekingU/rtdetr_r50vd")

inputs = image_processor(images=image, return_tensors="pt")

with torch.no_grad():
    outputs = model(**inputs)

results = image_processor.post_process_object_detection(outputs, target_sizes=torch.tensor([image.size[::-1]]), threshold=0.3)

for result in results:
    for score, label_id, box in zip(result["scores"], result["labels"], result["boxes"]):
        score, label = score.item(), label_id.item()
        box = [round(i, 2) for i in box.tolist()]
        print(f"{model.config.id2label[label]}: {score:.2f} {box}")
```
This should output
```
sofa: 0.97 [0.14, 0.38, 640.13, 476.21]
cat: 0.96 [343.38, 24.28, 640.14, 371.5]
cat: 0.96 [13.23, 54.18, 318.98, 472.22]
remote: 0.95 [40.11, 73.44, 175.96, 118.48]
remote: 0.92 [333.73, 76.58, 369.97, 186.99]
```

## Training Details

### Training Data

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The RTDETR model was trained on [COCO 2017 object detection](https://cocodataset.org/#download), a dataset consisting of 118k/5k annotated images for training/validation respectively. 

### Training Procedure

<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->

We conduct experiments on
COCO [20] and Objects365 [35], where RT-DETR is trained
on COCO train2017 and validated on COCO val2017
dataset. We report the standard COCO metrics, including
AP (averaged over uniformly sampled IoU thresholds ranging from 0.50-0.95 with a step size of 0.05), AP50, AP75, as
well as AP at different scales: APS, APM, APL.

#### Preprocessing [optional]

Images are resized/rescaled such that the shortest side is at 640 pixels.

#### Training Hyperparameters

- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->

![image/png](https://cdn-uploads.huggingface.co/production/uploads/6579e0eaa9e58aec614e9d97/E15I9MwZCtwNIms-W8Ra9.png)

#### Speeds, Sizes, Times [optional]

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[More Information Needed]

## Evaluation

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This model achieves an AP (average precision) of 53.1 on COCO 2017 validation. For more details regarding evaluation results, we refer to table 2 of the original paper.

### Testing Data, Factors & Metrics

#### Testing Data

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[More Information Needed]

#### Factors

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#### Metrics

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### Results

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#### Summary



## Model Examination [optional]

<!-- Relevant interpretability work for the model goes here -->

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## Environmental Impact

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Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).

- **Hardware Type:** [More Information Needed]
- **Hours used:** [More Information Needed]
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## Technical Specifications [optional]

### Model Architecture and Objective

![image/png](https://cdn-uploads.huggingface.co/production/uploads/6579e0eaa9e58aec614e9d97/sdIwTRlHNwPzyBNwHja60.png)

### Compute Infrastructure

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#### Hardware

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#### Software

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## Citation [optional]

<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->

**BibTeX:**

```bibtex
@misc{lv2023detrs,
      title={DETRs Beat YOLOs on Real-time Object Detection},
      author={Yian Zhao and Wenyu Lv and Shangliang Xu and Jinman Wei and Guanzhong Wang and Qingqing Dang and Yi Liu and Jie Chen},
      year={2023},
      eprint={2304.08069},
      archivePrefix={arXiv},
      primaryClass={cs.CV}
}
```

**APA:**

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## Glossary [optional]

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## More Information [optional]

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## Model Card Authors [optional]

[Sangbum Choi](https://huggingface.co/danelcsb)

## Model Card Contact

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