This repo consists of the source code for the ProxyNCA++ paper
Make sure to download the corresponding dataset to the correct folder as speci 5A54 fied in dataset/config.json We also include script to convert the dataset to hdf5 format.
To run the code
# CUB
CUDA_VISIBLE_DEVICES=0,1 python train.py --dataset cub --config config/cub.json --mode train --apex --seed 0
CUDA_VISIBLE_DEVICES=0,1 python train.py --dataset cub --config config/cub.json --mode trainval --apex --seed 0
# CARS
CUDA_VISIBLE_DEVICES=0,1 python train.py --dataset cars --config config/cars.json --mode train --apex --seed 0
CUDA_VISIBLE_DEVICES=0,1 python train.py --dataset cars --config config/cars.json --mode trainval --apex --seed 0
# SOP
CUDA_VISIBLE_DEVICES=0,1 python train.py --dataset sop --config config/sop.json --mode train --apex --seed 0
CUDA_VISIBLE_DEVICES=0,1 python train.py --dataset sop --config config/sop.json --mode trainval --apex --seed 0
# INSHOP
CUDA_VISIBLE_DEVICES=0,1 python train.py --dataset inshop --config config/inshop.json --mode trainval --apex --seed 0
The following is the Bibtex of our paper:
@article{teh2020proxynca++,
title={ProxyNCA++: Revisiting and Revitalizing Proxy Neighborhood Component Analysis},
author={Teh, Eu Wern and DeVries, Terrance and Taylor, Graham W},
journal={arXiv preprint arXiv:2004.01113},
year={2020}
}