# DINO: DETR with Improved DeNoising Anchor Boxes for End-to-End Object Detection## Introduction[DINO](https://arxiv.org/abs/2203.03605) is an object detection model based on DETR. We reproduced the model of the paper.## Model Zoo| Backbone | Model | Epochs | Box AP | Config | Download ||:------:|:---------------:|:------:|:------:|:---------------------------------------:|:--------------------------------------------------------------------------------:|| R-50 | dino_r50_4scale | 12 | 49.3 | [config](./dino_r50_4scale_1x_coco.yml) | [model](https://paddledet.bj.bcebos.com/models/dino_r50_4scale_1x_coco.pdparams) || R-50 | dino_r50_4scale | 24 | 50.8 | [config](./dino_r50_4scale_2x_coco.yml) | [model](https://paddledet.bj.bcebos.com/models/dino_r50_4scale_2x_coco.pdparams) |**Notes:**- DINO is trained on COCO train2017 dataset and evaluated on val2017 results of `mAP(IoU=0.5:0.95)`.- DINO uses 4GPU to train.GPU multi-card training```bashpython -m paddle.distributed.launch --gpus 0,1,2,3 tools/train.py -c configs/dino/dino_r50_4scale_1x_coco.yml --fleet--eval```## Custom Operator- Multi-scale deformable attention custom operator see [here](../../ppdet/modeling/transformers/ext_op).## Citations```@misc{zhang2022dino, title={DINO: DETR with Improved DeNoising Anchor Boxes for End-to-End Object Detection}, author={Hao Zhang and Feng Li and Shilong Liu and Lei Zhang and Hang Su and Jun Zhu and Lionel M. Ni and Heung-Yeung Shum}, year={2022}, eprint={2203.03605}, archivePrefix={arXiv}, primaryClass={cs.CV}}```