https://github.com/Megvii-BaseDetection/DisAlign

Implementation of "Distribution Alignment: A Unified Framework for Long-tail Visual Recognition"(CVPR 2021)
https://github.com/Megvii-BaseDetection/DisAlign

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

• Host: GitHub
• URL: https://github.com/Megvii-BaseDetection/DisAlign
• Owner: Megvii-BaseDetection
• License: apache-2.0
• Created: 2021-03-29T07:53:20.000Z (over 3 years ago)
• Default Branch: main
• Last Pushed: 2021-11-23T12:14:07.000Z (almost 3 years ago)
• Last Synced: 2024-04-17T00:18:46.864Z (7 months ago)
• Language: Python
• Size: 210 KB
• Stars: 114
• Watchers: 11
• Forks: 9
• Open Issues: 7
• Metadata Files:
  • Readme: README.md
  • License: LICENSE

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

[[Paper]](https://arxiv.org/abs/2103.16370)[[Code]](https://github.com/Megvii-BaseDetection/DisAlign)

We implement the classification, object detection and instance segmentation tasks based on our [cvpods](https://github.com/Megvii-BaseDetection/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](https://github.com/Megvii-BaseDetection/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](classification/README.md) 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++)](https://github.com/Megvii-BaseDetection/cvpods/blob/master/cvpods/layers/csrc/cocoeval/cocoeval.cpp) 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](https://github.com/achalddave/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](segmentation/README.md) 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](TODO) for the semantic segmentation experiments.

We will add the support for these experiments in [cvpods](https://github.com/Megvii-BaseDetection/cvpods) in the future.

# Acknowledgement

Thanks for the following projects:

- [cvpods](https://github.com/Megvii-BaseDetection/cvpods)

- [Detectron2](https://github.com/facebookresearch/detectron2)

- [mmsegmentation](https://github.com/open-mmlab/mmsegmentation)

- [classifier-balancing](https://github.com/facebookresearch/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.

```latex

@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.