PML: Progressive Margin Loss for Long-tailed Age Classification

This repository is the official implementation of paper: "PML: Progressive Margin Loss for Long-tailed Age Classification"(CVPR 2021) [Paper(CVF)] [Paper(arXiv)]

Datasets

[Morph II] [FG-NET] [ChaLearn LAP 2015] [IMDB-WIKI]

Training

We use (SNMC) Single Node Multi-GPU Cards training (with DistributedDataParallel) to get better performance.

 python -m torch.distributed.launch --nproc_per_node=2  --master_port 29502 train.py  --config configs/chalearn/exp_margin.yml

Testing

We test while training to save the best model, the results can be reproduced with the command below.

python test.py

Experiments

Citation

If you found this code or our work useful, please cite our paper.

@InProceedings{Deng_2021_CVPR,
    author    = {Deng, Zongyong and Liu, Hao and Wang, Yaoxing and Wang, Chenyang and Yu, Zekuan and Sun, Xuehong},
    title     = {PML: Progressive Margin Loss for Long-Tailed Age Classification},
    booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
    month     = {June},
    year      = {2021},
    pages     = {10503-10512}
}