Official PyTorch implementation of CVPR 2020 paper Proxy Anchor Loss for Deep Metric Learning.
A standard embedding network trained with Proxy-Anchor Loss achieves SOTA performance and most quickly converges.
This repository provides source code of experiments on four datasets (CUB-200-2011, Cars-196, Stanford Online Products and In-shop) and pretrained models.
- Python3
- PyTorch (> 1.0)
- NumPy
- tqdm
- wandb
- Pytorch-Metric-Learning
-
Download four public benchmarks for deep metric learning
- CUB-200-2011
- Cars-196 (Img, Annotation)
- Stanford Online Products (Link)
- In-shop Clothes Retrieval (Link)
-
Extract the tgz or zip file into
./data/(Exceptionally, for Cars-196, put the files in a./data/cars196)
(Notice!) I found that the link that was previously uploaded for the CUB dataset was incorrect, so I corrected the link. (CUB-200 -> CUB-200-2011) If you have previously downloaded the CUB dataset from my repository, please download it again. Thanks to myeongjun for reporting this issue!
Note that a sufficiently large batch size and good parameters resulted in better overall performance than that described in the paper. You can download the trained model through the hyperlink in the table.
- Train a embedding network of Inception-BN (d=512) using Proxy-Anchor loss
python train.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- Train a embedding network of ResNet-50 (d=512) using Proxy-Anchor loss
python train.py --gpu-id 0 \
--loss Proxy_Anchor \
--model resnet50 \
--embedding-size 512 \
--batch-size 120 \
--lr 1e-4 \
--dataset cub \
--warm 5 \
--bn-freeze 1 \
--lr-decay-step 5| Method | Backbone | R@1 | R@2 | R@4 | R@8 |
|---|---|---|---|---|---|
| Proxy-Anchor512 | Inception-BN | 69.1 | 78.9 | 86.1 | 91.2 |
| Proxy-Anchor512 | ResNet-50 | 69.9 | 79.6 | 86.6 | 91.4 |
- Train a embedding network of Inception-BN (d=512) using Proxy-Anchor loss
python train.py --gpu-id 0 \
--loss Proxy_Anchor \
--model bn_inception \
--embedding-size 512 \
--batch-size 180 \
--lr 1e-4 \
--dataset cars \
--warm 1 \
--bn-freeze 1 \
--lr-decay-step 20- Train a embedding network of ResNet-50 (d=512) using Proxy-Anchor loss
python train.py --gpu-id 0 \
--loss Proxy_Anchor \
--model resnet50 \
--embedding-size 512 \
--batch-size 120 \
--lr 1e-4 \
--dataset cars \
--warm 5 \
--bn-freeze 1 \
--lr-decay-step 10 | Method | Backbone | R@1 | R@2 | R@4 | R@8 |
|---|---|---|---|---|---|
| Proxy-Anchor512 | Inception-BN | 86.4 | 91.9 | 95.0 | 97.0 |
| Proxy-Anchor512 | ResNet-50 | 87.7 | 92.7 | 95.5 | 97.3 |
- Train a embedding network of Inception-BN (d=512) using Proxy-Anchor loss
python train.py --gpu-id 0 \
--loss Proxy_Anchor \
--model bn_inception \
--embedding-size 512 \
--batch-size 180 \
--lr 6e-4 \
--dataset SOP \
--warm 1 \
--bn-freeze 0 \
--lr-decay-step 20 \
--lr-decay-gamma 0.25| Method | Backbone | R@1 | R@10 | R@100 | R@1000 |
|---|---|---|---|---|---|
| Proxy-Anchor512 | Inception-BN | 79.2 | 90.7 | 96.2 | 98.6 |
- Train a embedding network of Inception-BN (d=512) using Proxy-Anchor loss
python train.py --gpu-id 0 \
--loss Proxy_Anchor \
--model bn_inception \
--embedding-size 512 \
--batch-size 180 \
--lr 6e-4 \
--dataset Inshop \
--warm 1 \
--bn-freeze 0 \
--lr-decay-step 20 \
--lr-decay-gamma 0.25| Method | Backbone | R@1 | R@10 | R@20 | R@30 | R@40 |
|---|---|---|---|---|---|---|
| Proxy-Anchor512 | Inception-BN | 91.9 | 98.1 | 98.7 | 99.0 | 99.1 |
Follow the below steps to evaluate the provided pretrained model or your 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 evaluate.py --gpu-id 0 \
--batch-size 120 \
--model bn_inception \
--embedding-size 512 \
--dataset cub \
--resume /set/your/model/path/best_model.pthOur code is modified and adapted on these great repositories:
Thanks Geonmo and nixingyang for the good implementation :D
If you use this method or this code in your research, please cite as:
@InProceedings{Kim_2020_CVPR,
author = {Kim, Sungyeon and Kim, Dongwon and Cho, Minsu and Kwak, Suha},
title = {Proxy Anchor Loss for Deep Metric Learning},
booktitle = {IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
month = {June},
year = {2020}
}
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