Official PyTorch implementation of CVPR 2021 paper Embedding Transfer with Label Relaxation for Improved Metric Learning.
Embedding trnasfer with Relaxed Contrastive Loss improves performance, or reduces sizes and output dimensions of embedding model effectively.
This repository provides source code of experiments on three datasets (CUB-200-2011, Cars-196 and Stanford Online Products) including relaxed contrastive loss, relaxed MS loss, and 6 other knowledge distillation or embedding transfer methods such as:
Download three public benchmarks for deep metric learning.
Extract the tgz or zip file into ./data/
(Exceptionally, for Cars-196, put the files in a ./data/cars196
)
Download the pretrained source models using ./scripts/download_pretrained_source_models.sh
.
sh scripts/download_pretrained_source_models.sh
python code/train_target.py --gpu-id 0 --loss Relaxed_Contra --model bn_inception \
--embedding-size 512 --batch-size 90 --IPC 2 --dataset cub --epochs 90 \
--source-ckpt ./pretrained_source/bn_inception/cub_bn_inception_512dim_Proxy_Anchor_ckpt.pth \
--view 2 --sigma 1 --delta 1 --save 1
python code/train_target.py --gpu-id 0 --loss Relaxed_Contra --model bn_inception \
--embedding-size 512 --batch-size 90 --IPC 2 --dataset cars --epochs 90 \
--source-ckpt ./pretrained_source/bn_inception/cars_bn_inception_512dim_Proxy_Anchor_ckpt.pth \
--view 2 --sigma 1 --delta 1 --save 1
python code/train_target.py --gpu-id 0 --loss Relaxed_Contra --model bn_inception \
--embedding-size 512 --batch-size 90 --IPC 2 --dataset SOP --epochs 150 \
--source-ckpt ./pretrained_source/bn_inception/SOP_bn_inception_512dim_Proxy_Anchor_ckpt.pth \
--view 2 --sigma 1 --delta 1 --save 1
CUB-200-2011 | Cars-196 | SOP | ||||||||
---|---|---|---|---|---|---|---|---|---|---|
Method | Backbone | R@1 | R@2 | R@4 | R@1 | R@2 | R@4 | R@1 | R@2 | R@4 |
Source: PA | BN512 | 69.1 | 78.9 | 86.1 | 86.4 | 91.9 | 95.0 | 79.2 | 90.7 | 96.2 |
FitNet | BN512 | 69.9 | 79.5 | 86.2 | 87.6 | 92.2 | 95.6 | 78.7 | 90.4 | 96.1 |
Attention | BN512 | 66.3 | 76.2 | 84.5 | 84.7 | 90.6 | 94.2 | 78.2 | 90.4 | 96.2 |
CRD | BN512 | 67.7 | 78.1 | 85.7 | 85.3 | 91.1 | 94.8 | 78.1 | 90.2 | 95.8 |
DarkRank | BN512 | 66.7 | 76.5 | 84.8 | 84.0 | 90.0 | 93.8 | 75.7 | 88.3 | 95.3 |
PKT | BN512 | 69.1 | 78.8 | 86.4 | 86.4 | 91.6 | 94.9 | 78.4 | 90.2 | 96.0 |
RKD | BN512 | 70.9 | 80.8 | 87.5 | 88.9 | 93.5 | 96.4 | 78.5 | 90.2 | 96.0 |
Ours | BN512 | 72.1 | 81.3 | 87.6 | 89.6 | 94.0 | 96.5 | 79.8 | 91.1 | 96.3 |
python code/train_target.py --gpu-id 0 --loss Relaxed_Contra --model bn_inception \
--embedding-size 64 --batch-size 90 --IPC 2 --dataset cub --epochs 90 \
--source-ckpt ./pretrained_source/bn_inception/cub_bn_inception_512dim_Proxy_Anchor_ckpt.pth \
--view 2 --sigma 1 --delta 1 --save 1
python code/train_target.py --gpu-id 0 --loss Relaxed_Contra --model bn_inception \
--embedding-size 64 --batch-size 90 --IPC 2 --dataset cars --epochs 90 \
--source-ckpt ./pretrained_source/bn_inception/cars_bn_inception_512dim_Proxy_Anchor_ckpt.pth \
--view 2 --sigma 1 --delta 1 --save 1
python code/train_target.py --gpu-id 0 --loss Relaxed_Contra --model bn_inception \
--embedding-size 64 --batch-size 90 --IPC 2 --dataset SOP --epochs 150 \
--source-ckpt ./pretrained_source/bn_inception/SOP_bn_inception_512dim_Proxy_Anchor_ckpt.pth \
--view 2 --sigma 1 --delta 1 --save 1
CUB-200-2011 | Cars-196 | SOP | ||||||||
---|---|---|---|---|---|---|---|---|---|---|
Method | Backbone | R@1 | R@2 | R@4 | R@1 | R@2 | R@4 | R@1 | R@2 | R@4 |
Source: PA | BN512 | 69.1 | 78.9 | 86.1 | 86.4 | 91.9 | 95.0 | 79.2 | 90.7 | 96.2 |
FitNet | BN64 | 62.3 | 73.8 | 83.0 | 81.2 | 87.7 | 92.5 | 76.6 | 89.3 | 95.4 |
Attention | BN64 | 58.3 | 69.4 | 79.1 | 79.2 | 86.7 | 91.8 | 76.3 | 89.2 | 95.4 |
CRD | BN64 | 60.9 | 72.7 | 81.7 | 79.2 | 87.2 | 92.1 | 75.5 | 88.3 | 95.3 |
DarkRank | BN64 | 63.5 | 74.3 | 83.1 | 78.1 | 85.9 | 91.1 | 73.9 | 87.5 | 94.8 |
PKT | BN64 | 63.6 | 75.8 | 84.0 | 82.2 | 88.7 | 93.5 | 74.6 | 87.3 | 94.2 |
RKD | BN64 | 65.8 | 76.7 | 85.0 | 83.7 | 89.9 | 94.1 | 70.2 | 83.8 | 92.1 |
Ours | BN64 | 67.4 | 78.0 | 85.9 | 86.5 | 92.3 | 95.3 | 76.3 | 88.6 | 94.8 |
python code/train_target.py --gpu-id 0 --loss Relaxed_Contra --model resnet18 \
--embedding-size 128 --batch-size 90 --IPC 2 --dataset cub --epochs 90 \
--source-ckpt ./pretrained_source/resnet50/cub_resnet50_512dim_Proxy_Anchor_ckpt.pth \
--view 2 --sigma 1 --delta 1 --save 1
python code/train_target.py --gpu-id 0 --loss Relaxed_Contra --model resnet18 \
--embedding-size 128 --batch-size 90 --IPC 2 --dataset cars --epochs 90 \
--source-ckpt ./pretrained_source/resnet50/cars_resnet50_512dim_Proxy_Anchor_ckpt.pth \
--view 2 --sigma 1 --delta 1 --save 1
python code/train_target.py --gpu-id 0 --loss Relaxed_Contra --model resnet18 \
--embedding-size 128 --batch-size 90 --IPC 2 --dataset SOP --epochs 150 \
--source-ckpt ./pretrained_source/resnet50/SOP_resnet50_512dim_Proxy_Anchor_ckpt.pth \
--view 2 --sigma 1 --delta 1 --save 1
CUB-200-2011 | Cars-196 | SOP | ||||||||
---|---|---|---|---|---|---|---|---|---|---|
Method | Backbone | R@1 | R@2 | R@4 | R@1 | R@2 | R@4 | R@1 | R@2 | R@4 |
Source: PA | R50512 | 69.9 | 79.6 | 88.6 | 87.7 | 92.7 | 95.5 | 80.5 | 91.8 | 98.8 |
FitNet | R18128 | 61.0 | 72.2 | 81.1 | 78.5 | 86.0 | 91.4 | 76.7 | 89.4 | 95.5 |
Attention | R18128 | 61.0 | 71.7 | 81.5 | 78.6 | 85.9 | 91.0 | 76.4 | 89.3 | 95.5 |
CRD | R18128 | 62.8 | 73.8 | 83.2 | 80.6 | 87.9 | 92.5 | 76.2 | 88.9 | 95.3 |
DarkRank | R18128 | 61.2 | 72.5 | 82.0 | 75.3 | 83.6 | 89.4 | 72.7 | 86.7 | 94.5 |
PKT | R18128 | 65.0 | 75.6 | 84.8 | 81.6 | 88.8 | 93.4 | 76.9 | 89.2 | 95.5 |
RKD | R18128 | 65.8 | 76.3 | 84.8 | 84.2 | 90.4 | 94.3 | 75.7 | 88.4 | 95.1 |
Ours | R18128 | 66.6 | 78.1 | 85.9 | 86.0 | 91.6 | 95.3 | 78.4 | 90.4 | 96.1 |
This repository also provides code for training source embedding network with several losses as well as proxy-anchor loss. For details on how to train the source embedding network, please see the Proxy-Anchor Loss repository.
python code/train_source.py --gpu-id 0 --loss Proxy_Anchor --model bn_inception \
--embedding-size 512 --batch-size 180 --lr 1e-4 --dataset cub \
--warm 1 --bn-freeze 1 --lr-decay-step 10
Follow the below steps to evaluate the trained model. \
Trained best model will be saved in the ./logs/folder_name
.
# The parameters should be changed according to the model to be evaluated.
python code/evaluate.py --gpu-id 0 \
--batch-size 120 \
--model bn_inception \
--embedding-size 512 \
--dataset cub \
--ckpt /set/your/model/path/best_model.pth
Our source code is modified and adapted on these great repositories:
If you use this method or this code in your research, please cite as:
@inproceedings{kim2021embedding,
title={Embedding Transfer with Label Relaxation for Improved Metric Learning},
author={Kim, Sungyeon and Kim, Dongwon and Cho, Minsu and Kwak, Suha},
booktitle={Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition},
year={2021}
}