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Author: KaihuaTang

README.md

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-This project provides a strong single-stage baseline for Long-Tailed Classification (under ImageNet-LT, Long-Tailed CIFAR-10/-100 datasets), Detection, and Instance Segmentation (under LVIS dataset). It is also a PyTorch implementation of the **NeurIPS 2020 paper** [Long-Tailed Classification by Keeping the Good and Removing the Bad Momentum Causal Effect](), which proposes a general solution to remove the bad momentum causal effect for a variety of Long-Tailed Recognition tasks. The codes are organized into three folders: 
+This project provides a strong single-stage baseline for Long-Tailed Classification (under ImageNet-LT, Long-Tailed CIFAR-10/-100 datasets), Detection, and Instance Segmentation (under LVIS dataset). It is also a PyTorch implementation of the **NeurIPS 2020 paper** [Long-Tailed Classification by Keeping the Good and Removing the Bad Momentum Causal Effect](https://kaihuatang.github.io/Files/long-tail.pdf), which proposes a general solution to remove the bad momentum causal effect for a variety of Long-Tailed Recognition tasks. The codes are organized into three folders: 
 1. The [classification folder](classification) supports long-tailed classification on ImageNet-LT, Long-Tailed CIFAR-10/CIFAR-100 datasets.
 2. The [lvis_old folder (deprecated)](lvis_old) supports long-tailed object detection and instance segmentation on LVIS V0.5 dataset, which is built on top of mmdet V1.1.
 3. The latest version of long-tailed detection and instance segmentation is under [lvis1.0 folder](lvis1.0). Since both LVIS V0.5 and mmdet V1.1 are no longer available on their homepages, we have to re-implement our method on [mmdet V2.4](https://github.com/open-mmlab/mmdetection) using [LVIS V1.0 annotations](https://www.lvisdataset.org/dataset).