tjddus9597 / Proxy Anchor Cvpr2020
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Proxy Anchor Loss for Deep Metric Learning
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.
[email protected] versus training time on the Cars-196
Accuracy inRequirements
- Python3
- PyTorch (> 1.0)
- NumPy
- tqdm
- wandb
- Pytorch-Metric-Learning
Datasets
-
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!
Training Embedding Network
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.
CUB-200-2011
- 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 | [email protected] | [email protected] | [email protected] | [email protected] |
---|---|---|---|---|---|
Proxy-Anchor512 | Inception-BN | 69.1 | 78.9 | 86.1 | 91.2 |
Proxy-Anchor512 | ResNet-50 | 69.9 | 79.6 | 86.6 | 91.4 |
Cars-196
- 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 | [email protected] | [email protected] | [email protected] | [email protected] |
---|---|---|---|---|---|
Proxy-Anchor512 | Inception-BN | 86.4 | 91.9 | 95.0 | 97.0 |
Proxy-Anchor512 | ResNet-50 | 87.7 | 92.7 | 95.5 | 97.3 |
Stanford Online Products
- 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 | [email protected] | [email protected] | [email protected] | [email protected] |
---|---|---|---|---|---|
Proxy-Anchor512 | Inception-BN | 79.2 | 90.7 | 96.2 | 98.6 |
In-Shop Clothes Retrieval
- 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 | [email protected] | [email protected] | [email protected] | [email protected] | [email protected] |
---|---|---|---|---|---|---|
Proxy-Anchor512 | Inception-BN | 91.9 | 98.1 | 98.7 | 99.0 | 99.1 |
Evaluating Image Retrieval
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.pth
Acknowledgements
Our code is modified and adapted on these great repositories:
Other Implementations
Thanks Geonmo and nixingyang for the good implementation :D
Citation
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}
}