---
license: apache-2.0
library_name: libreyolo
tags:
  - object-detection
  - rt-detr
  - transformer
  - real-time
  - pytorch
datasets:
  - coco
---

# LibreYOLO RT-DETRv2-M*

RT-DETRv2-M* (r34vd) - 49.9 AP on COCO

## Model Details

- **Architecture**: RT-DETRv2 (Real-Time Detection Transformer v2)
- **Backbone**: ResNet-34 (r34vd)
- **Framework**: PyTorch
- **License**: Apache 2.0

## Performance

| Model | Dataset | Input Size | AP | AP50 | Params | FPS |
|-------|---------|------------|-----|------|--------|-----|
| RT-DETRv2-M* | COCO | 640 | 49.9 | 67.5 | 31M | 161 |

## Usage

```python
from libreyolo import LIBREYOLO

# Load model
model = LIBREYOLO("librertdetrms.pth", size="ms")

# 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)