Awesome

Implementation of "Distribution Alignment: A Unified Framework for Long-tail Visual Recognition"(CVPR 2021)

[Paper][Code]

We implement the classification, object detection and instance segmentation tasks based on our cvpods. The users should install cvpods first and run the experiments in this repo.

Changelog

• 7.28.2021 Update the DisAlign on ImageNet-LT(ResX50)
• 4.23.2021 Update the DisAlign on LVIS v0.5(Mask R-CNN + Res50)
• 4.12.2021 Update the README

0. How to Use

• Step-1: Install the latest cvpods.
• Step-2: cd cvpods
• Step-3: Prepare dataset for different tasks.
• Step-4: git clone https://github.com/Megvii-BaseDetection/DisAlign playground_disalign
• Step-5: Enter one folder and run pods_train --num-gpus 8
• Step-6: Use pods_test --num-gpus 8 to evaluate the last the checkpoint

1. Image Classification

We support the the following three datasets:

• ImageNet-LT Dataset
• iNaturalist-2018 Dataset
• Place-LT Dataset

We refer the user to CLS_README for more details.

2. Object Detection/Instance Segmentation

We support the two versions of the LVIS dataset:

• LVIS v0.5
• LVIS v1.0

Highlight

1. To speedup the evaluation on LVIS dataset, we provide the C++ optimized evaluation api by modifying the coco_eval(C++) in cvpods.

• The C++ version lvis_eval API will save ~30% time when calculating the mAP.

2. We provide support for the metric of AP_fixed and AP_pool proposed in large-vocab-devil
3. We will support more recent works on long-tail detection in this project(e.g. EQLv2, CenterNet2, etc.) in the future.

We refer the user to DET_README for more details.

3. Semantic Segmentation

We adopt the mmsegmentation as the codebase for runing all experiments of DisAlign. Currently, the user should use DisAlign_Seg for the semantic segmentation experiments. We will add the support for these experiments in cvpods in the future.

Acknowledgement

Thanks for the following projects:

• cvpods
• Detectron2
• mmsegmentation
• classifier-balancing

Citing DisAlign

If you are using the DisAlign in your research or with to refer to the baseline results publised in this repo, please use the following BibTex entry.

@inproceedings{zhang2021disalign,
  title={Distribution Alignment: A Unified Framework for Long-tail Visual Recognition.},
  author={Zhang, Songyang and Li, Zeming and Yan, Shipeng and He, Xuming and Sun, Jian},
  booktitle={CVPR},
  year={2021}
}

License

This repo is released under the Apache 2.0 license. Please see the LICENSE file for more information.