Ecosyste.ms: Awesome
An open API service indexing awesome lists of open source software.
https://github.com/ljin0429/HIST
Hypergraph-Induced Semantic Tuplet Loss for Deep Metric Learning [CVPR'22]
https://github.com/ljin0429/HIST
cvpr2022 deep-learning graph-neural-networks metric-learning pytorch
Last synced: about 2 months ago
JSON representation
Hypergraph-Induced Semantic Tuplet Loss for Deep Metric Learning [CVPR'22]
- Host: GitHub
- URL: https://github.com/ljin0429/HIST
- Owner: ljin0429
- Created: 2022-03-14T01:43:18.000Z (over 2 years ago)
- Default Branch: main
- Last Pushed: 2022-04-27T15:41:16.000Z (over 2 years ago)
- Last Synced: 2024-04-21T03:43:19.495Z (5 months ago)
- Topics: cvpr2022, deep-learning, graph-neural-networks, metric-learning, pytorch
- Language: Python
- Homepage:
- Size: 29.3 KB
- Stars: 17
- Watchers: 1
- Forks: 2
- Open Issues: 2
-
Metadata Files:
- Readme: README.md
Awesome Lists containing this project
README
# Hypergraph-Induced Semantic Tuplet Loss for Deep Metric Learning (CVPR 2022)
Official PyTorch implementation of **HIST loss** for deep metric learning | paper (The paper link will be updated soon!)
This repository provides 1) source codes for the main results and 2) pre-trained models for quick evaluation.
## Requirements
* Python3
* PyTorch
* [PyTorch Metric Learning](https://github.com/KevinMusgrave/pytorch-metric-learning)
* Numpy
* tqdm
* pandas
* matplotlib
* wandb (optional)
## Installation
We recommend using Conda (or Virtualenv) to set up an environment.
Our implementation was tested on the following libraries with Python 3.6.
* Install PyTorch:
```
pip install torch==1.7.1+cu101 torchvision==0.8.2+cu101 torchaudio==0.7.2 -f https://download.pytorch.org/whl/torch_stable.html
```
* Install the other dependencies:
```
pip install tqdm
pip install pandas
pip install matplotlib
pip install pytorch-metric-learning
pip install wandb
```
## Dataset preparation
Download three public benchmarks for deep metric learning, and extract the tgz or zip files into `./data/`.
* [CUB-200-2011](http://www.vision.caltech.edu/datasets/cub_200_2011/)
* [Cars-196](http://ai.stanford.edu/~jkrause/cars/car_dataset.html)
* [Stanford Online Products](https://cvgl.stanford.edu/projects/lifted_struct/)
**(Note)** For Cars-196, download both a tar of all images and annotations for both training and test images from the website, and then, put the files into `./data/cars196`.
## Training
Our HIST loss utilizes multilateral semantic relations between every sample and class for a given mini-batch via hypergraph modeling
(see `./code/hist.py`).
By leveraging multilateral semantic relations, HIST loss enables the embedding network to capture important visual semantics suitable for deep metric learning.
A standard embedding network (*e.g.*, ResNet-50) trained with our HIST loss (see `./code/train.py`) achieves SOTA performance on three public benckmarks for deep metric learning.
### CUB-200-2011
* Train an embedding network of ResNet-50 (D=512) using HIST loss:
```bash
python train.py --gpu-id 0
--dataset cub
--model resnet50
--embedding-size 512
--tau 32
--alpha 1.1
--epochs 40
--lr 1.2e-4
--lr-ds 1e-1
--lr-hgnn-factor 5
--weight-decay 5e-5
--lr-decay-step 5
```
### Cars-196
* Train an embedding network of ResNet-50 (D=512) using HIST loss:
```bash
python train.py --gpu-id 0
--dataset cars
--model resnet50
--embedding-size 512
--tau 32
--alpha 0.9
--epochs 50
--lr 1e-4
--lr-ds 1e-1
--lr-hgnn-factor 10
--weight-decay 1e-4
--lr-decay-step 10
```
### Stanford Online Products
* Train an embedding network of ResNet-50 (D=512) using HIST loss:
```bash
python train.py --gpu-id 0
--dataset SOP
--model resnet50
--embedding-size 512
--tau 16
--alpha 2
--epochs 60
--lr 1e-4
--lr-ds 1e-2
--lr-hgnn-factor 10
--weight-decay 1e-4
--lr-decay-step 10
--bn-freeze 0
```
## Evaluation
For an evaluation demo, we provide our pre-trained ResNet-50 (D=512) using HIST loss.
* Download our torch models as follows:
```bash
# CUB-200-2011
wget https://github.com/ljin0429/HIST/releases/download/torchmodel/cub_resnet50_best.pth
# Cars-196
wget https://github.com/ljin0429/HIST/releases/download/torchmodel/cars_resnet50_best.pth
# Standord Online Products
wget https://github.com/ljin0429/HIST/releases/download/torchmodel/SOP_resnet50_best.pth
```
* Evaluate the provided pre-trained model or your own trained model:
```bash
# The parameters should be changed according to the model to be evaluated.
python evaluate.py --gpu-id 0
--dataset (cub/cars/SOP)
--model resnet50
--model-path (your_model_path)
```
## Acknowledgements
Our code was implemented, built upon the following great repositories:
* [Proxy Anchor Loss for Deep Metric Learning](https://github.com/tjddus9597/Proxy-Anchor-CVPR2020) (CVPR 2020)
* [Hypergraph Neural Networks](https://github.com/iMoonLab/HGNN) (AAAI 2019)
* [PyTorch Metric Learning](https://github.com/KevinMusgrave/pytorch-metric-learning)
## Citation