modified results (should follow cropping results)

+1M results is updated.

• 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

1. Find datasets via symlinks from datasets/data to the actual locations where the dataset images and annotations are stored. Refer to DATA.md.

2. 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

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

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

2. 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

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

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

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}
}

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)