BBN: Bilateral-Branch Network with Cumulative Learning for Long-Tailed Visual Recognition

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)

Main requirements

• torch == 1.0.1
• torchvision == 0.2.2_post3
• tensorboardX == 1.8
• Python 3

Pretrain models for iNaturalist

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

Usage

# 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.

Data format

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

Citing this repository

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}
}

Contacts

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