Symmetrical Synthesis for Deep Metric Learning
Official Tensorflow implementation of Symmetrical Synthesis for Deep Metric Learning (AAAI 2020)
Geonmo Gu*, Byungsoo Ko* (* Authors contributed equally.)
@NAVER/LINE Vision
Overview
Symmetrical Synthesis
Symmetrical Synthesis (Symm) is a novel method of synthetic hard sample generation for deep metric learning.
How it Works
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.
Experimental Results
Getting Started
Requirements
$ pip3 install -r requirements.txt
Prepare Data
- 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://ai.stanford.edu/~jkrause/car196/car_ims.tgz
$ tar -xzf car_ims.tgz
$ mv car_ims
$ wget http://ai.stanford.edu/~jkrause/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
Train a Model
- Available losses: N-pair, Symm + N-pair, Angular, Symm + Angular
Symm + N-pair
$ 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
Test a Model
$ 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)
Check Test Results
$ tensorboard --logdir=eval_log_car --port=10000
Acknowledgements
- Googlenet backbone
Citation
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}
}
License
Copyright (c) 2020-present NAVER Corp.
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