更换文档检测模型
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paddle_detection/configs/group_detr/README.md
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paddle_detection/configs/group_detr/README.md
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# Group DETR: Fast DETR training with group-wise one-to-many assignment
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# Group DETR v2: Strong object detector with encoder-decoder pretraining
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## Introduction
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[Group DETR](https://arxiv.org/pdf/2207.13085.pdf) is an object detection model based on DETR. We reproduced the model of the paper.
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[Group DETR v2](https://arxiv.org/pdf/2211.03594.pdf) is a strong object detection model based on DINO and Group DETR. We reproduced the model of the paper.
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## Model Zoo
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| Backbone | Model | Epochs | Resolution |Box AP | Config | Download |
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|:------:|:---------------:|:------:|:------:|:---------------------------------------:|:--------------------------------------------------------------------------------:|:------:|
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| R-50 | dino_r50_4scale | 12 | (800, 1333) | 49.6 | [config](./group_dino_r50_4scale_1x_coco.yml) | [model](https://bj.bcebos.com/v1/paddledet/models/group_dino_r50_4scale_1x_coco.pdparams) |
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| Vit-huge | dino_vit_huge_4scale | 12 | (1184, 2000) | 63.3 | [config](./group_dino_vit_huge_4scale_1x_coco.yml) | [model](https://bj.bcebos.com/v1/paddledet/models/group_dino_vit_huge_4scale_1x_coco.pdparams) |
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**Notes:**
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- Group DETR is trained on COCO train2017 dataset and evaluated on val2017 results of `mAP(IoU=0.5:0.95)`.
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- Group DETRv2 requires a ViT-Huge encoder pre-trained and fine-tuned on ImageNet-1K in a self-supervised manner, a detector pre-trained on Object365, and finally it is fine-tuned on trainCOCO. Group DETRv2 is also evaluated on val2017 results of `mAP(IoU=0.5:0.95)`.
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- Group DETR and Group DETRv2 are both use 4GPU to train.
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GPU multi-card training
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```bash
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python -m paddle.distributed.launch --gpus 0,1,2,3 tools/train.py -c configs/group_detr/group_dino_r50_4scale_1x_coco.yml --fleet --eval
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```
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```bash
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python -m paddle.distributed.launch --gpus 0,1,2,3 tools/train.py -c configs/group_detr/group_dino_vit_huge_4scale_1x_coco.yml --fleet --eval
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```
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## Citations
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```
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@article{chen2022group,
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title={Group DETR: Fast DETR training with group-wise one-to-many assignment},
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author={Chen, Qiang and Chen, Xiaokang and Wang, Jian and Feng, Haocheng and Han, Junyu and Ding, Errui and Zeng, Gang and Wang, Jingdong},
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journal={arXiv preprint arXiv:2207.13085},
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volume={1},
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number={2},
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year={2022}
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}
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@article{chen2022group,
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title={Group DETR v2: Strong object detector with encoder-decoder pretraining},
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author={Chen, Qiang and Wang, Jian and Han, Chuchu and Zhang, Shan and Li, Zexian and Chen, Xiaokang and Chen, Jiahui and Wang, Xiaodi and Han, Shuming and Zhang, Gang and others},
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journal={arXiv preprint arXiv:2211.03594},
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year={2022}
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}
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```
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