Boyan Zhou, Quan Cui, Xiu-Shen Wei*, Zhao-Min Chen
This repository is the official PyTorch implementation of paper BBN: Bilateral-Branch Network with Cumulative Learning for Long-Tailed Visual Recognition. (The work has been accepted by CVPR2020, Oral Presentation)
- torch == 1.0.1
- torchvision == 0.2.2_post3
- tensorboardX == 1.8
- Python 3
We provide the BBN pretrain models of both 1x scheduler and 2x scheduler for iNaturalist 2018 and iNaturalist 2017.
iNaturalist 2018: Baidu Cloud, Google Drive
iNaturalist 2017: Baidu Cloud, Google Drive
# To train long-tailed CIFAR-10 with imbalanced ratio of 50:
python main/train.py --cfg configs/cifar10.yaml
# To validate with the best model:
python main/valid.py --cfg configs/cifar10.yaml
# To debug with CPU mode:
python main/train.py --cfg configs/cifar10.yaml CPU_MODE True
You can change the experimental setting by simply modifying the parameter in the yaml file.
The annotation of a dataset is a dict consisting of two field: annotations
and num_classes
.
The field annotations
is a list of dict with
image_id
, fpath
, im_height
, im_width
and category_id
.
Here is an example.
{
'annotations': [
{
'image_id': 1,
'fpath': '/home/BBN/iNat18/images/train_val2018/Plantae/7477/3b60c9486db1d2ee875f11a669fbde4a.jpg',
'im_height': 600,
'im_width': 800,
'category_id': 7477
},
...
]
'num_classes': 8142
}
You can use the following code to convert from the original format of iNaturalist. The images and annotations can be downloaded at iNaturalist 2018 and iNaturalist 2017
# Convert from the original format of iNaturalist
python tools/convert_from_iNat.py --file train2018.json --root /home/iNat18/images --sp /home/BBN/jsons
If you find this code useful in your research, please consider citing us:
@article{zhou2020BBN,
title={{BBN}: Bilateral-Branch Network with Cumulative Learning for Long-Tailed Visual Recognition},
author={Boyan Zhou and Quan Cui and Xiu-Shen Wei and Zhao-Min Chen},
booktitle={CVPR},
pages={1--8},
year={2020}
}
If you have any questions about our work, please do not hesitate to contact us by emails.
Xiu-Shen Wei: weixs.gm@gmail.com
Boyan Zhou: zhouboyan94@gmail.com
Quan Cui: cui-quan@toki.waseda.jp