SoftTriple Loss
PyTorch Implementation for Our ICCV'19 Paper: "SoftTriple Loss: Deep Metric Learning Without Triplet Sampling"
Usage: Train on Cars196
Here is an example of using this package.
- Obtain dataset
wget http://imagenet.stanford.edu/internal/car196/car_ims.tgz
tar -xf car_ims.tgz
- Generate train/test sets
python genCars.py
- Learn 64-dimensional embeddings
python train.py --gpu 0 --dim 64 -C 98 --freeze_BN [folder with train and test folders]
Requirements
- Python 3.7
- PyTorch 1.1
- scikit-learn 0.20.1
Citation
If you use the package in your research, please cite our paper:
@inproceedings{qian2019striple,
author = {Qi Qian and
Lei Shang and
Baigui Sun and
Juhua Hu and
Hao Li and
Rong Jin},
title = {SoftTriple Loss: Deep Metric Learning Without Triplet Sampling},
booktitle = {{IEEE} International Conference on Computer Vision, {ICCV} 2019},
year = {2019}
}