Paddle Implementation of DOLG: Single-Stage Image Retrieval with Deep Orthogonal Fusion of Local and Global Features (ICCV 2021)
- 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.
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 |
-
Infer-model-weights (f3gf)
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}
}