Group DETR: Fast DETR training with group-wise one-to-many assignment
Group DETR v2: Strong object detector with encoder-decoder pretraining
Introduction
Group DETR is an object detection model based on DETR. We reproduced the model of the paper.
Group DETR v2 is a strong object detection model based on DINO and Group DETR. We reproduced the model of the paper.
Model Zoo
| Backbone | Model | Epochs | Resolution | Box AP | Config | Download |
|---|---|---|---|---|---|---|
| R-50 | dino_r50_4scale | 12 | (800, 1333) | 49.6 | config | model |
| Vit-huge | dino_vit_huge_4scale | 12 | (1184, 2000) | 63.3 | config | model |
Notes:
- Group DETR is trained on COCO train2017 dataset and evaluated on val2017 results of
mAP(IoU=0.5:0.95). - 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). - Group DETR and Group DETRv2 are both use 4GPU to train.
GPU multi-card training
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
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
Citations
@article{chen2022group,
title={Group DETR: Fast DETR training with group-wise one-to-many assignment},
author={Chen, Qiang and Chen, Xiaokang and Wang, Jian and Feng, Haocheng and Han, Junyu and Ding, Errui and Zeng, Gang and Wang, Jingdong},
journal={arXiv preprint arXiv:2207.13085},
volume={1},
number={2},
year={2022}
}
@article{chen2022group,
title={Group DETR v2: Strong object detector with encoder-decoder pretraining},
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},
journal={arXiv preprint arXiv:2211.03594},
year={2022}
}