MMT: Mixed Mutual Transfer for Long-Tailed Image Classification

Ning Ren, Xiaosong Li, Yanxia Wu*, Yan Fu

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 3080ti GPU card for training and testing.

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/MMT/CIFAIR-10-LT/images/train/7477/3b60c9486db1d2ee875f11a669fbde4a.jpg',
                        'im_height': 600,
                        'im_width': 800,
                        'category_id': 7477
                    },
                    ...
                   ]
    'num_classes': 8142
}

Contacts

If you have any questions about our work, please do not hesitate to contact us by emails.

Ning.Ren.hrbeu@outlook.com

lixiaosong@hrbeu.edu.cn

wuyanxia@hrbeu.edu.cn

fuyan@hrbeu.edu.cn