/HDhE

Heterogeneous Double-Head Ensemble for Deep Metric Learning (IEEE Access 2020).

Primary LanguagePython

Heterogeneous Double-Head Ensemble for Deep Metric Learning

Official Pytorch implementation of paper:

Heterogeneous Double-Head Ensemble for Deep Metric Learning (IEEE Access 2020).

Environment

Python 3.6, Pytorch 0.4.1, Torchvision, tensorboard

Train

Default setting:

  • Architecture: ResNet-50
  • Dataset: CUB2011 (or Cars-196, Inshop, SOP)
  • Batch size: 40
  • Image size: 224X224

prepare

The dataset path should be changed to your own path.

CUB2011-200 dataset are available on https://drive.google.com/file/d/1hbzc_P1FuxMkcabkgn9ZKinBwW683j45/view

Cars-196 dataset are available on https://ai.stanford.edu/~jkrause/cars/car_dataset.html

prepare_cub.py

train network.

The dataset path(data_dir='/home/ro/FG/CUB_200_2011/pytorch') should be changed to your own path.

HDhE_train.py --dataset CUB-200

In the case of Cars-196 retrieval dataset training,

HDhE_train.py --dataset Cars-196

Results (CUB-200-2011)

Methods R@1 R@2 R@4 R@8
CUB-200 73.90% 83.05 89.64 93.69

Citation

@ARTICLE{9123761,
  author={Y. {Ro} and J. Y. {Choi}},
  journal={IEEE Access}, 
  title={Heterogeneous Double-Head Ensemble for Deep Metric Learning}, 
  year={2020},
  volume={8},
  number={},
  pages={118525-118533},
  doi={10.1109/ACCESS.2020.3004579}}

Youngmin Ro, Jin Young Choi, "Heterogeneous Double-Head Ensemble for Deep Metric Learning", IEEE Access, 2020.