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--- |
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license: apache-2.0 |
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library_name: libreyolo |
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tags: |
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- object-detection |
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- rt-detr |
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- transformer |
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- real-time |
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- pytorch |
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datasets: |
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- coco |
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--- |
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# LibreYOLO RT-DETRv2-M* |
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RT-DETRv2-M* (r34vd) - 49.9 AP on COCO |
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## Model Details |
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- **Architecture**: RT-DETRv2 (Real-Time Detection Transformer v2) |
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- **Backbone**: ResNet-34 (r34vd) |
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- **Framework**: PyTorch |
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- **License**: Apache 2.0 |
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## Performance |
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| Model | Dataset | Input Size | AP | AP50 | Params | FPS | |
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|-------|---------|------------|-----|------|--------|-----| |
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| RT-DETRv2-M* | COCO | 640 | 49.9 | 67.5 | 31M | 161 | |
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## Usage |
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```python |
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from libreyolo import LIBREYOLO |
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# Load model |
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model = LIBREYOLO("librertdetrms.pth", size="ms") |
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# Run inference |
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result = model("image.jpg", conf_thres=0.5) |
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# Access results |
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print(f"Detected {result['num_detections']} objects") |
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for box, score, cls in zip(result['boxes'], result['scores'], result['classes']): |
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print(f" Class {cls}: {score:.2f} @ {box}") |
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``` |
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## Installation |
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```bash |
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pip install libreyolo |
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``` |
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## Citation |
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```bibtex |
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@misc{lv2024rtdetrv2, |
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title={RT-DETRv2: Improved Baseline with Bag-of-Freebies for Real-Time Detection Transformer}, |
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author={Wenyu Lv and Yian Zhao and Qinyao Chang and Kui Huang and Guanzhong Wang and Yi Liu}, |
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year={2024}, |
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eprint={2407.17140}, |
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archivePrefix={arXiv}, |
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primaryClass={cs.CV} |
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} |
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``` |
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## Links |
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- [LibreYOLO GitHub](https://github.com/Libre-YOLO/libreyolo) |
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- [RT-DETR Paper](https://arxiv.org/abs/2407.17140) |
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- [Original RT-DETR Repo](https://github.com/lyuwenyu/RT-DETR) |
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