Official Tensorflow implementation of Symmetrical Synthesis for Deep Metric Learning (AAAI 2020)
Geonmo Gu*, Byungsoo Ko* (* Authors contributed equally.)
@Clova Vision, NAVER/LINE Corp.
Symmetrical Synthesis (Symm) is a novel method of synthetic hard sample generation for deep metric learning.
After some iterations, symmetrical synthesis generates synthetic points around the class clusters, which are used as hard samples to push the other class with stronger power.
$ pip3 install -r requirements.txt
- Download pretrained GoogleNet model. ref
$ wget https://github.com/Wei2624/Feature_Embed_GoogLeNet/raw/master/tf_ckpt_from_caffe.mat
- Download CAR DB and cook.
$ wget http://imagenet.stanford.edu/internal/car196/car_ims.tgz
$ tar -xzf car_ims.tgz
$ mv car_ims
$ wget http://imagenet.stanford.edu/internal/car196/cars_annos.mat
# on ../symm_public folder
$ cd dataset
$ python3 cooking_CARS.py --car_folder=/your/car_ims/folder \
--save_path=/your/converted/carDB/will/be/saved/here
- Available losses: N-pair, Symm + N-pair, Angular, Symm + Angular
$ python3 train.py --backbone=googlenet \
--pretrained_model_path=/your/folder/tf_ckpt_from_caffe.mat \
--image_path=/your/converted/carDB/will/be/saved/here \
--run_gpu=0 \
--save_path=/your/trained/model/will/be/saved/here \
--losses=symm_npair --dim_features=512 \
--input_size=227 --learning_rate=0.0001 \
--decay_steps=5000 --decay_stop_steps=15000 \
--decay_stop_value=0.00001 --decay_ratio=0.5 \
--save_model_steps=100
$ python3 test.py --run_gpu=1 --model_path=/your/trained/model/will/be/saved/here \
--image_path=/your/converted/carDB/will/be/saved/here \
--batch_size=512 --backbone=googlenet \
--pretrained_model_path=/your/folder/tf_ckpt_from_caffe.mat \
--log_path=eval_log_car \
--input_size=227 --start_idx=0 --dim_features=512
- Best recall@1: 0.77 (0.765 in paper)
$ tensorboard --logdir=eval_log_car --port=10000
- Googlenet backbone
If you find Symmetrical Synthesis useful in your research, please consider to cite the following paper.
@inproceedings{gu2020symmetrical,
title={Symmetrical Synthesis for Deep Metric Learning},
author={Geonmo Gu and Byungsoo Ko},
booktitle={Proceedings of the AAAI Conference on Artificial Intelligence},
year={2020}
}
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