yangjun dfa27afb39 提交PaddleDetection develop 分支 d56cf3f7c294a7138013dac21f87da4ea6bee829 | il y a 1 an | |
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_base_ | il y a 1 an | |
README.md | il y a 1 an | |
dino_r50_4scale_1x_coco.yml | il y a 1 an | |
dino_r50_4scale_2x_coco.yml | il y a 1 an |
DINO is an object detection model based on DETR. We reproduced the model of the paper.
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:
mAP(IoU=0.5:0.95)
.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
@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}
}