This is the official implementation of the paper "DETRs with Hybrid Matching".
Authors: Ding Jia, Yuhui Yuan, Haodi He, Xiaopei Wu, Haojun Yu, Weihong Lin, Lei Sun, Chao Zhang, Han Hu
We provide a set of baseline results and trained models available for download:
Name | Backbone | query | epochs | AP | download |
---|---|---|---|---|---|
Deformable-DETR + tricks | R50 | 300 | 24 | 32.2 | model |
Deformable-DETR + tricks | SwinB | 300 | 48 | 44.6 | model |
Deformable-DETR + tricks | SwinL | 300 | 48 | 47.0 | model |
H-Deformable-DETR + tricks | R50 | 300 | 24 | 33.5 | model |
H-Deformable-DETR + tricks | SwinB | 300 | 48 | 46.0 | model |
H-Deformable-DETR + tricks | SwinL | 300 | 48 | 47.9 | model |
See install instructions.
See prepare datasets.
DETECTRON2_DATASETS=<datasets_path> python train_net.py --num-gpus 8 --config-file <config_file>
To train/eval a model with the swin transformer backbone, you need to download the backbone from the offical repo frist and specify argument--pretrained_backbone_path
like our configs.
DETECTRON2_DATASETS=<datasets_path> python train_net.py --num-gpus 8 --resume --config-file <config_file> --eval-only MODEL.WEIGHTS /path/to/weight.pth
We modified detic/modeling/meta_arch/d2_deformable_detr.py
to support one-to-many matching loss.
Other modifications are under third_party/Deformable-DETR
, for more information, please see here.
If you find H-Detic-LVIS useful in your research, please consider citing:
@article{jia2022detrs,
title={DETRs with Hybrid Matching},
author={Jia, Ding and Yuan, Yuhui and He, Haodi and Wu, Xiaopei and Yu, Haojun and Lin, Weihong and Sun, Lei and Zhang, Chao and Hu, Han},
journal={arXiv preprint arXiv:2207.13080},
year={2022}
}
@inproceedings{zhou2021detecting,
title={Detecting Twenty-thousand Classes using Image-level Supervision},
author={Zhou, Xingyi and Girdhar, Rohit and Joulin, Armand and Kr{\"a}henb{\"u}hl, Philipp and Misra, Ishan},
booktitle={arXiv preprint arXiv:2201.02605},
year={2021}
}
This repo is modified based on Detic.