Paddle Implementation of DOLG: Single-Stage Image Retrieval with Deep Orthogonal Fusion of Local and Global Features (ICCV 2021)

Training

• Pytorch Version: There are increasing inquiries for Pytorch codes and models, we have had our pytorch models and codes ready at this url. We are happy to see from a Chinese technical media post (https://mp.weixin.qq.com/s/7B3hZUpLtTt8NcGt0c-77w) that our DOLG has been adopted as one of building blocks to the Kaggle21 landmark competition winner solution . In this post, third-party Pytorch code snippets of DOLG are also presented.

Evaluation

cd revisitop && python example_evaluate.py

modified results from torch weights

	Roxf-M	+1M	Rpar-M	+1M	Roxf-H	+1M	Rpar-H	+1M
DOLG-R50(with query cropping)	81.20	71.36	90.07	78.99	62.55	47.34	79.20	59.75
DOLG-R101(with query cropping)	82.37	73.63	90.97	80.44	64.93	51.57	81.71	62.95
DOLG-R50(w/o query cropping)	82.38	77.78	90.94	82.16	62.92	55.48	80.48	65.77
DOLG-R101(w/o query cropping)	83.22	78.96	91.64	82.89	64.83	57.86	82.56	67.34

Weights

• R101-DOLG (a25u) R50-DOLG (1on8)

• Infer-model-weights (f3gf)

Citation

If the project helps your research, please consider citing our paper as follows.

@InProceedings{Yang_2021_ICCV,
    author={Yang, Min and He, Dongliang and Fan, Miao and Shi, Baorong and Xue, Xuetong and Li, Fu and Ding, Errui and Huang, Jizhou},
    title={DOLG: Single-Stage Image Retrieval With Deep Orthogonal Fusion of Local and Global Features},
    booktitle={Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV)},
    month={October},
    year={2021},
    pages={11772-11781}
}