Official Code of AAAI 2021 Paper "Multi-level Distance Regularization for Deep Metric Learning", * Yonghyun Kim and * Wonpyo Park. (* Equal Contribution)
You need a CUDA-enabled GPU and python (>3.6) to run the source code.
- torchvision >= 0.4.2
- torch >= 1.3.1
- tqdm
- scipy
- Pillow
pip install -r requirements.txt
mkdir ./dataset
The source code will automatically download CUB-200-2011, Cars-196, and Stanford Online Products datasets.
But you need to manually download In-Shop Clothes Retrieval dataset.
- Make
Inshopdirectory in./datasetdirectory
mkdir -p ./dataset/Inshop
- Download
img.zipat the following link, and unzip it inInshopdirectory
https://drive.google.com/drive/folders/0B7EVK8r0v71pYkd5TzBiclMzR00
- Download
list_eval_partition.txtat the following link, and put it in theInshopdirectory.
https://drive.google.com/drive/folders/0B7EVK8r0v71pWVBJelFmMW5EWnM
# The models are trained with Triplet+MDR, please check Table 1.
# CUB-200-2011
wget https://github.com/anonymous-ai-research/pretrained/raw/master/cub200/cub200.pth
python run.py --mode eval --dataset cub200 --load cub200.pth
# Cars-196
wget https://github.com/anonymous-ai-research/pretrained/raw/master/cars196/cars196.pth
python run.py --mode eval --dataset cars196 --load cars196.pth
# Stanford Online Products
wget https://github.com/anonymous-ai-research/pretrained/raw/master/sop/sop.pth
python run.py --mode eval --dataset stanford --load sop.pth
# In-Shop Clothes Retrieval
wget https://github.com/anonymous-ai-research/pretrained/raw/master/inshop/inshop.pth
python run_inshop.py --mode eval --load inshop.pth# CUB-200-2011
# Triplet
python run.py --dataset cub200 --lr 5e-5 --recall 1 2 4 8
# Triplet+L2Norm
python run.py --dataset cub200 --lr 5e-5 --recall 1 2 4 8 --l2norm
# Triplet+MDR
python run.py --dataset cub200 --lr 5e-5 --recall 1 2 4 8 --lambda-mdr 0.6 --nu-mdr 0.01
# Cars-196
# Triplet
python run.py --dataset cars196 --lr 5e-5 --recall 1 2 4 8
# Triplet+L2Norm
python run.py --dataset cars196 --lr 5e-5 --recall 1 2 4 8 --l2norm
# Triplet+MDR
python run.py --dataset cars196 --lr 5e-5 --recall 1 2 4 8 --lambda-mdr 0.2 --nu-mdr 0.01
# Stanford Online Products
# Triplet
python run.py --dataset stanford --num-image-per-class 3 --batch 256 --lr 1e-4 --recall 1 10 100 1000
# Triplet+L2Norm
python run.py --dataset stanford --num_image_per_class 3 --batch 256 --lr 1e-4 --recall 1 10 100 1000 --l2norm
# Triplet+MDR
python run.py --dataset stanford --num-image-per-class 3 --batch 256 --lr 1e-4 --recall 1 10 100 1000 --lambda-mdr 0.1 --nu-mdr 0.01
# In-Shop Clothes Retrieval
# Triplet
python run_inshop.py --num-image-per-class 3 --batch 256 --lr 1e-4 --recall 1 10 20 30 40
# Triplet+L2Norm
python run_inshop.py --num-image-per-class 3 --batch 256 --lr 1e-4 --recall 1 10 20 30 40 --l2norm
# Triplet+MDR
python run_inshop.py --num-image-per-class 3 --batch 256 --lr 1e-4 --recall 1 10 20 30 40 --lambda-mdr 0.1 --nu-mdr 0.01If you use this method or this code in your research, please cite as:
@InProceedings{Kim_2021_AAAI,
author = {Kim, Yonghyun and Park, Wonpyo},
title = {Multi-level Distance Regularization for Deep Metric Learning},
booktitle = {Proceedings of the 35th AAAI Conference on Artificial Intelligence},
year = {2021}
}
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