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---
license: apache-2.0
library_name: libreyolo
tags:
- object-detection
- rt-detr
- transformer
- real-time
- pytorch
datasets:
- coco
---
# LibreYOLO RT-DETRv2-X
RT-DETRv2-X (r101vd) - 54.3 AP on COCO
## Model Details
- **Architecture**: RT-DETRv2 (Real-Time Detection Transformer v2)
- **Backbone**: ResNet-101 (r101vd)
- **Framework**: PyTorch
- **License**: Apache 2.0
## Performance
| Model | Dataset | Input Size | AP | AP50 | Params | FPS |
|-------|---------|------------|-----|------|--------|-----|
| RT-DETRv2-X | COCO | 640 | 54.3 | 72.8 | 76M | 74 |
## Usage
```python
from libreyolo import LIBREYOLO
# Load model
model = LIBREYOLO("librertdetrx.pth", size="x")
# Run inference
result = model("image.jpg", conf_thres=0.5)
# Access results
print(f"Detected {result['num_detections']} objects")
for box, score, cls in zip(result['boxes'], result['scores'], result['classes']):
print(f" Class {cls}: {score:.2f} @ {box}")
```
## Installation
```bash
pip install libreyolo
```
## Citation
```bibtex
@misc{lv2024rtdetrv2,
title={RT-DETRv2: Improved Baseline with Bag-of-Freebies for Real-Time Detection Transformer},
author={Wenyu Lv and Yian Zhao and Qinyao Chang and Kui Huang and Guanzhong Wang and Yi Liu},
year={2024},
eprint={2407.17140},
archivePrefix={arXiv},
primaryClass={cs.CV}
}
```
## Links
- [LibreYOLO GitHub](https://github.com/Libre-YOLO/libreyolo)
- [RT-DETR Paper](https://arxiv.org/abs/2407.17140)
- [Original RT-DETR Repo](https://github.com/lyuwenyu/RT-DETR)
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