https://github.com/wutong16/DistributionBalancedLoss

[ ECCV 2020 Spotlight ] Pytorch implementation for "Distribution-Balanced Loss for Multi-Label Classification in Long-Tailed Datasets"
https://github.com/wutong16/DistributionBalancedLoss

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[ ECCV 2020 Spotlight ] Pytorch implementation for "Distribution-Balanced Loss for Multi-Label Classification in Long-Tailed Datasets"

• Host: GitHub
• URL: https://github.com/wutong16/DistributionBalancedLoss
• Owner: wutong16
• Created: 2020-06-22T07:33:27.000Z (over 4 years ago)
• Default Branch: master
• Last Pushed: 2022-07-14T13:35:00.000Z (about 2 years ago)
• Last Synced: 2024-07-22T14:38:37.786Z (2 months ago)
• Language: Python
• Homepage:
• Size: 20.3 MB
• Stars: 355
• Watchers: 8
• Forks: 45
• Open Issues: 5
• Metadata Files:
  • Readme: README.md

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README

        
# Distribution-Balanced Loss

[[Paper]](https://arxiv.org/abs/2007.09654)

The implementation of our paper *Distribution-Balanced Loss for Multi-Label Classification in Long-Tailed Datasets* (ECCV2020 **Spotlight**).

 

[Tong Wu](https://github.com/wutong16), [Qingqiu Huang](http://qqhuang.cn/), [Ziwei Liu](https://liuziwei7.github.io/), [Yu Wang](http://nicsefc.ee.tsinghua.edu.cn/people/yu-wang/), [Dahua Lin](http://dahua.me/). 

 



## Requirements 

* [Pytorch](https://pytorch.org/)

* [Sklearn](https://scikit-learn.org/stable/)

## Installation

```

git clone [email protected]:wutong16/DistributionBalancedLoss.git

cd DistributionBalancedLoss

```

## Quick start

### Training

#### COCO-MLT

```

python tools/train.py configs/coco/LT_resnet50_pfc_DB.py 

```

#### VOC-MLT

```

python tools/train.py configs/voc/LT_resnet50_pfc_DB.py 

```

### Testing

#### COCO-MLT

```

bash tools/dist_test.sh configs/coco/LT_resnet50_pfc_DB.py work_dirs/LT_coco_resnet50_pfc_DB/epoch_8.pth 1

```

#### VOC-MLT

```

bash tools/dist_test.sh configs/voc/LT_resnet50_pfc_DB.py work_dirs/LT_voc_resnet50_pfc_DB/epoch_8.pth 1

```

## Pre-trained models

#### COCO-MLT

|   Backbone  |    Total   |    Head   |  Medium  |   Tail  |      Download      |

| :---------: | :------------: | :-----------: | :---------: | :---------: | :----------------: |

|  ResNet-50  |      53.55      |      51.13     |    57.05     |     51.06    |     [model](https://drive.google.com/file/d/1HPQMmPVfqiDUTmzrTxNv3clhYa662QKb/view?usp=sharing)      |

####  VOC-MLT

|   Backbone  |    Total   |    Head   |  Medium  |   Tail  |      Download      |

| :---------: | :------------: | :-----------: | :---------: | :---------: | :----------------: |

|  ResNet-50  |      78.94      |      73.22     |    84.18     |     79.30    |     [model](https://drive.google.com/file/d/1jGHiCfQKDNjdYxjKXfp8ifFadW2BuGWm/view?usp=sharing)      |

## Datasets



### Use our dataset

The long-tail multi-label datasets we use in the paper are created from [MS COCO](https://cocodataset.org/) 2017 and [Pascal VOC](http://host.robots.ox.ac.uk/pascal/VOC/) 2012. Annotations and statistics data resuired when training are saved under `./appendix` in this repo.

```

appendix

  |--coco

    |--longtail2017

      |--class_freq.pkl

      |--class_split.pkl

      |--img_id.pkl

  |--VOCdevkit

    |--longtail2012

      |--class_freq.pkl

      |--class_split.pkl

      |--img_id.pkl

```

### Try your own

You can also create a new long-tailed dataset by downloading the annotations, `terse_gt_2017.pkl` for COCO and `terse_gt_2012.pkl` for VOC, from [here](https://drive.google.com/drive/folders/1B7-GODp-HDH24OzEafCIV4IfAJ_R7NuE?usp=sharing) and move them into the right folders as below.

```

appendix

  |--coco

    |--longtail2017

      |--terse_gt_2017.pkl

  |--VOCdevkit

    |--longtail2012

      |--terse_gt_2012.pkl

```

Then run the following command, adjust the parameters as you like to control the distribution.

```

python tools/create_longtail_dataset.py

```

To update the corresponding `class_freq.pkl` files, please refer to `def _save_info` in `.\mllt\datasets\custom.py`.

## License and Citation

The use of this software is RESTRICTED to **non-commercial research and educational purposes**.

```

@inproceedings{DistributionBalancedLoss,

  title={Distribution-Balanced Loss for Multi-Label Classification in Long-Tailed Datasets},

  author={Wu, Tong and Huang, Qingqiu and Liu, Ziwei and Wang, Yu and Lin, Dahua},

  booktitle={European Conference on Computer Vision (ECCV)},

  year={2020}

}

```

## TODO

- [ ] Distributed training is not supported currently

- [ ] Evaluation with single GPU is not supported currently

- [ ] test pytorch 0.4.0

## Contact

This repo is currently maintained by [@wutong16](https://github.com/wutong16) and [@hqqasw](https://github.com/hqqasw)