iq-scm / bbn

The official PyTorch implementation of paper BBN: Bilateral-Branch Network with Cumulative Learning for Long-Tailed Visual Recognition

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

Environmental settings

This repository is developed using python 3.5.2/3.6.7 on Ubuntu 16.04.5 LTS. The CUDA nad CUDNN version is 9.0 and 7.1.3 respectively. For Cifar experiments, we use one NVIDIA 1080ti GPU card for training and testing. (four cards for iNaturalist ones). Other platforms or GPU cards are not fully tested.

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: [email protected]

Boyan Zhou: [email protected]

Quan Cui: [email protected]