add rtdetrv2 #9073
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# Conflicts: # configs/rtdetrv2/README.md # configs/rtdetrv2/_base_/rtdetrv2_reader.yml
lyuwenyu
reviewed
Jul 27, 2024
configs/rtdetrv2/README.md
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| primaryClass={cs.CV} | ||
| } | ||
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| @software{Lv_rtdetr_by_cvperception_2023, |
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这引用更新下 把software这个换成 v2的文章+
@misc{lv2024rtdetrv2improvedbaselinebagoffreebies,
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},
url={https://arxiv.org/abs/2407.17140},
}
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lyuwenyu
reviewed
Jul 29, 2024
configs/rtdetrv2/README.md
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| <details> | ||
| <summary>3. 转换成TensorRT（可选） </summary> | ||
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| - 确保TensorRT的版本>=8.5.1 |
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离散采样的模型 是不需要这个版本要求的；可以再准确描述下
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lyuwenyu
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Jul 30, 2024
configs/rtdetrv2/README.md
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| RT-DETRv2是基于 Transformer 的实时端到端检测器。它在SOTA的 RT-DETR | ||
| 的基础上，引入了灵活的解码器，并运用了一系列有效的训练策略。具体而言，我们为解码器的各种特征图建议了不同数量的采样点，在多个训练阶段采用动态数据增强策略，并为每个独特的模型确定特定的优化超参数。为适应各种部署方案，解码器现在提供了一个利用离散采样而非网格采样的选项。RT-DETRv2-R18 | ||
| 在相同速度下相比 RT-DETR-R18 实现了 1.4 的提升，在 T4 GPU 上以 FP16 模式达到了 47.9 mAP 和 217 | ||
| FPS。而且，混合精度训练策略的使用使得训练速度提高了 15%，GPU 内存使用减少了 20% |
LokeZhou
approved these changes
Jul 30, 2024
This was referenced Jul 30, 2024
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add rtdetrv2