Awesome
Distribution-Balanced Loss
The implementation of our paper Distribution-Balanced Loss for Multi-Label Classification in Long-Tailed Datasets (ECCV2020 Spotlight).
Tong Wu<sup></sup>, Qingqiu Huang, Ziwei Liu<sup></sup>, Yu Wang, Dahua Lin.
<img src='./assets/overall.png' width=800>Requirements
Installation
git clone git@github.com: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 |
VOC-MLT
| Backbone | Total | Head | Medium | Tail | Download |
|---|---|---|---|---|---|
| ResNet-50 | 78.94 | 73.22 | 84.18 | 79.30 | model |
Datasets
<img src='./assets/dataset.png' width=400>Use our dataset
The long-tail multi-label datasets we use in the paper are created from MS COCO 2017 and 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 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