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

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.

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


  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.
  1. We provide support for the metric of AP_fixed and AP_pool proposed in large-vocab-devil
  2. 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.


Thanks for the following projects:

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.

  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},