Awesome

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

Pipeline

Performances

modified results (should follow cropping results)

	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

+1M results is updated.

Codes

Requirements

NVIDIA GPU, Linux, Python3(tested on 3.6.10)
Tested with CUDA 10.2, cuDNN 7.1 and PyTorch 1.4.0

pip install -r requirements.txt

Training

Find datasets via symlinks from datasets/data to the actual locations where the dataset images and annotations are stored. Refer to DATA.md.
Set datapath, model, training parameters in configs/resnet101_delg_8gpu.yaml and run

python train.py \
    --cfg configs/resnet101_delg_8gpu.yaml \
    OUT_DIR ./output \
    PORT 13001 \
    TRAIN.WEIGHTS ./pretrained/R-101-1x64d_dds_8gpu.pyth

Evaluation

ROxf and RPar feature extraction, set ${total_num}=1 and run

python evaler/infer.py --cfg configs/resnet101_delg_8gpu.yaml

1M distractor feature extraction, set ${total_num} = n * ${gpu_cards} in configs/resnet101_delg_8gpu.yaml and run

sh scripts/run_extractor.sh configs/resnet101_delg_8gpu.yaml

Eval on ROxf and RPar, refer README.md for data fetch and description. Groudtruth file and some examples are prepared in revisitop.

Wights

R-50-DOLG (d7or) - R-101-DOLG (nqdb)

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}
}

References

pycls(https://github.com/facebookresearch/pycls) pymetric(https://github.com/feymanpriv/pymetric) DELG(https://github.com/feymanpriv/DELG) Parsing-R-CNN(https://github.com/soeaver/Parsing-R-CNN)