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README.md

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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)