# Deformable DETR ## Introduction Deformable DETR is an object detection model based on DETR. We reproduced the model of the paper. ## Model Zoo | Backbone | Model | Images/GPU | Inf time (fps) | Box AP | Config | Download | |:------:|:--------:|:--------:|:--------------:|:------:|:------:|:--------:| | R-50 | Deformable DETR | 2 | --- | 44.1 | [config](https://github.com/PaddlePaddle/PaddleDetection/blob/release/2.4/configs/deformable_detr/deformable_detr_r50_1x_coco.yml) | [model](https://paddledet.bj.bcebos.com/models/deformable_detr_r50_1x_coco.pdparams) | **Notes:** - Deformable DETR is trained on COCO train2017 dataset and evaluated on val2017 results of `mAP(IoU=0.5:0.95)`. - Deformable DETR uses 8GPU to train 50 epochs. GPU multi-card training ```bash export CUDA_VISIBLE_DEVICES=0,1,2,3,4,5,6,7 python -m paddle.distributed.launch --gpus 0,1,2,3,4,5,6,7 tools/train.py -c configs/deformable_detr/deformable_detr_r50_1x_coco.yml --fleet ``` ## Citations ``` @inproceedings{ zhu2021deformable, title={Deformable DETR: Deformable Transformers for End-to-End Object Detection}, author={Xizhou Zhu and Weijie Su and Lewei Lu and Bin Li and Xiaogang Wang and Jifeng Dai}, booktitle={International Conference on Learning Representations}, year={2021}, url={https://openreview.net/forum?id=gZ9hCDWe6ke} } ```