This repository contains the code for reproducing the results for Divide and Conquer the Embedding Space for Metric Learning (CVPR 2019) with the datasets In-Shop Clothes, Stanford Online Products and PKU VehicleID.
We also applied our method to the Humpback Whale Identification Challenge at Kaggle and finished at 10th place out of 2131.
Slides: link
The following command will train the model with Margin loss on the In-Shop Clothes dataset for 200 epochs and a batch size of 80 while splitting the embedding layer with 8 clusters and finetuning the model from epoch 190 on. You can use this command to reproduce the results of the paper for the three datasets by changing simply --dataset=inshop to --dataset=sop (Stanford Online Products) or --dataset=vid (Vehicle-ID).
CUDA_VISIBLE_DEVICES=0 python experiment.py --dataset=inshop \
--dir=test --exp=0 --random-seed=0 --nb-clusters=8 --nb-epochs=200 \
--sz-batch=80 --backend=faiss-gpu --embedding-lr=1e-5 --embedding-wd=1e-4 \
--backbone-lr=1e-5 --backbone-wd=1e-4 --finetune-epoch=190The model can be trained without the proposed method by setting the number of clusters to 1 with --nb-clusters=1.
For faster clustering we run Faiss on GPU. If you installed Faiss without GPU support use flag --backend=faiss.
The model checkpoints and log files are saved in the selected log-directory. You can print a summary of the results with python browse_results <log path>.
You will get slightly higher results than what we have reported in the paper. For SOP, In-Shop and Vehicle-ID the R@1 results should be somewhat around 76.40, 87.36 and 91.54.
You may find out more about the license here
If you use this code, please cite the following paper:
Artsiom Sanakoyeu, Vadim Tschernezki, Uta Büchler, Björn Ommer. "Divide and Conquer the Embedding Space for Metric Learning", CVPR 2019.
@InProceedings{dcesml,
title={Divide and Conquer the Embedding Space for Metric Learning},
author={Sanakoyeu, Artsiom and Tschernezki, Vadim and B\"uchler, Uta and Ommer, Bj\"orn},
booktitle={Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition},
year={2019},
}