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

• Paper | Supplementary Video | Poster | Spotlight PPT

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

1. Download pretrained GoogleNet model. ref

$ wget https://github.com/Wei2624/Feature_Embed_GoogLeNet/raw/master/tf_ckpt_from_caffe.mat

2. 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
  • https://github.com/wzzheng/HDML/blob/master/lib/GoogleNet_Model.py
  • https://github.com/Wei2624/Feature_Embed_GoogLeNet

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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