Divide and Conquer the Embedding Space for Metric Learning
About
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
Requirements
- Python version 3.6.6 or higher
- SciPy and scikit-learn packages
- PyTorch (pytorch.org)
- Faiss with GPU support (Faiss)
- download and extract the datasets for In-Shop Clothes, Stanford Online Products and PKU VehicleID
Usage
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=190
The 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.
Expected Results
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.
Related Repos
- Collection of baselines for metric learning from @Confusezius [PyTorch]
License
You may find out more about the license here
Reference
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},
}