Awesome

Equalization Loss for Long-Tailed Object Recognition

Jingru Tan, Changbao Wang, Buyu Li, Quanquan Li, Wanli Ouyang, Changqing Yin, Junjie Yan

:warning: We recommend to use the EQLv2 repository (code) which is based on mmdetection. It also includes EQL and other algorithms, such as cRT (classifier-retraining), BAGS (BalanceGroup Softmax).

[arXiv] [BibTeX]

In this repository, we release code for Equalization Loss (EQL) in Detectron2. EQL protects the learning for rare categories from being at a disadvantage during the network parameter updating under the long-tailed situation.

Installation

Install Detectron 2 following INSTALL.md. You are ready to go!

LVIS Dataset

Following the instruction of README.md to set up the lvis dataset.

Training

To train a model with 8 GPUs run:

cd /path/to/detectron2/projects/EQL
python train_net.py --config-file configs/eql_mask_rcnn_R_50_FPN_1x.yaml --num-gpus 8

Evaluation

Model evaluation can be done similarly:

cd /path/to/detectron2/projects/EQL
python train_net.py --config-file configs/eql_mask_rcnn_R_50_FPN_1x.yaml --eval-only MODEL.WEIGHTS /path/to/model_checkpoint

Pretrained Models

Instance Segmentation on LVIS

The AP in this repository is higher than that of the origin paper. Because all those models use:

• Scale jitter
• Class-specific mask head
• Better ImageNet pretrain models (of caffe rather than pytorch)

Note that the final results of these configs have large variance across different runs.

<a name="CitingEQL"></a>Citing EQL

If you use EQL, please use the following BibTeX entry.

@InProceedings{tan2020eql,
  title={Equalization Loss for Long-Tailed Object Recognition},
  author={Jingru Tan, Changbao Wang, Buyu Li, Quanquan Li, 
  Wanli Ouyang, Changqing Yin, Junjie Yan},
  journal={ArXiv:2003.05176},
  year={2020}
}