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https://github.com/tjddus9597/Proxy-Anchor-CVPR2020
Official PyTorch Implementation of Proxy Anchor Loss for Deep Metric Learning, CVPR 2020
https://github.com/tjddus9597/Proxy-Anchor-CVPR2020
cvpr2020 deep-metric-learning image-retrieval proxy-anchor-loss pytorch
Last synced: 3 months ago
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Official PyTorch Implementation of Proxy Anchor Loss for Deep Metric Learning, CVPR 2020
- Host: GitHub
- URL: https://github.com/tjddus9597/Proxy-Anchor-CVPR2020
- Owner: tjddus9597
- License: mit
- Created: 2020-04-07T15:09:10.000Z (over 4 years ago)
- Default Branch: master
- Last Pushed: 2022-05-17T01:45:29.000Z (over 2 years ago)
- Last Synced: 2024-01-26T04:36:59.912Z (10 months ago)
- Topics: cvpr2020, deep-metric-learning, image-retrieval, proxy-anchor-loss, pytorch
- Language: Python
- Size: 99.6 KB
- Stars: 303
- Watchers: 9
- Forks: 60
- Open Issues: 3
-
Metadata Files:
- Readme: README.md
- License: LICENSE
Awesome Lists containing this project
README
# Proxy Anchor Loss for Deep Metric Learning
Official PyTorch implementation of CVPR 2020 paper [**Proxy Anchor Loss for Deep Metric Learning**](https://arxiv.org/abs/2003.13911).
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.
### Accuracy in Recall@1 versus training time on the Cars-196
## Requirements
- Python3
- PyTorch (> 1.0)
- NumPy
- tqdm
- wandb
- [Pytorch-Metric-Learning](https://github.com/KevinMusgrave/pytorch-metric-learning)
## Datasets
1. Download four public benchmarks for deep metric learning
- [CUB-200-2011](http://www.vision.caltech.edu/visipedia-data/CUB-200-2011/CUB_200_2011.tgz)
- Cars-196 ([Img](http://imagenet.stanford.edu/internal/car196/car_ims.tgz), [Annotation](http://imagenet.stanford.edu/internal/car196/cars_annos.mat))
- Stanford Online Products ([Link](https://cvgl.stanford.edu/projects/lifted_struct/))
- In-shop Clothes Retrieval ([Link](http://mmlab.ie.cuhk.edu.hk/projects/DeepFashion.html))
2. 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**
```bash
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**
```bash
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](https://drive.google.com/file/d/1twaY6S2QIR8eanjDB6PoVPlCTsn-6ZJW/view?usp=sharing) | Inception-BN | 69.1 | 78.9 | 86.1 | 91.2 |
| [Proxy-Anchor512](https://drive.google.com/file/d/1s-cRSEL2PhPFL9S7bavkrD_c59bJXL_u/view?usp=sharing) | 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**
```bash
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**
```bash
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](https://drive.google.com/file/d/1wwN4ojmOCEAOaSYQHArzJbNdJQNvo4E1/view?usp=sharing) | Inception-BN | 86.4 | 91.9 | 95.0 | 97.0 |
| [Proxy-Anchor512](https://drive.google.com/file/d/1_4P90jZcDr0xolRduNpgJ9tX9HZ1Ih7n/view?usp=sharing) | 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**
```bash
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](https://drive.google.com/file/d/1hBdWhLP2J83JlOMRgZ4LLZY45L-9Gj2X/view?usp=sharing) | 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**
```bash
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](https://drive.google.com/file/d/1VE7psay7dblDyod8di72Sv7Z2xGtUGra/view?usp=sharing) | 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`.
```bash
# 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:
- [No Fuss Distance Metric Learning using Proxies](https://github.com/dichotomies/proxy-nca)
- [PyTorch Metric learning](https://github.com/KevinMusgrave/pytorch-metric-learning)
## Other Implementations
- [Pytorch, Tensorflow and Mxnet implementations](https://github.com/geonm/proxy-anchor-loss)
- [Keras implementations](https://github.com/nixingyang/Proxy-Anchor-Loss)
Thanks Geonmo and nixingyang for the good implementation :D
## New Method for Further Improvement
Recently, our paper **Embedding Transfer with Label Relaxation for Improved Metric Learning** which presents the new knowledge distillation method for metric learning is accepted and will be presented at CVPR21.
Our new method can greatly improve the performance, or reduce sizes and output dimensions of embedding networks with negligible performance degradation.
If you are also interested in new knowlege distillation method for metric learning, please check the following arxiv and repository links.
The new repository has been refactored based on the Proxy-Anchor Loss implementation, so those who have used this repository will be able to use new code easily. :D
- [Arxiv](https://arxiv.org/pdf/2103.14908.pdf) | [Github](https://github.com/tjddus9597/LabelRelaxation-CVPR21)
## 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}
}