yangjun dfa27afb39 提交PaddleDetection develop 分支 d56cf3f7c294a7138013dac21f87da4ea6bee829 1 년 전
..
_base_ dfa27afb39 提交PaddleDetection develop 分支 d56cf3f7c294a7138013dac21f87da4ea6bee829 1 년 전
README.md dfa27afb39 提交PaddleDetection develop 分支 d56cf3f7c294a7138013dac21f87da4ea6bee829 1 년 전
dino_r50_4scale_1x_coco.yml dfa27afb39 提交PaddleDetection develop 分支 d56cf3f7c294a7138013dac21f87da4ea6bee829 1 년 전
dino_r50_4scale_2x_coco.yml dfa27afb39 提交PaddleDetection develop 分支 d56cf3f7c294a7138013dac21f87da4ea6bee829 1 년 전

README.md

DINO: DETR with Improved DeNoising Anchor Boxes for End-to-End Object Detection

Introduction

DINO 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 49.1 config model
R-50 dino_r50_4scale 24 50.5 config model

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

python -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.

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}
}