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RIDE: Long-tailed Recognition by Routing Diverse Distribution-Aware Experts.

by Xudong Wang, Long Lian, Zhongqi Miao, Ziwei Liu and Stella X. Yu at UC Berkeley, ICSI and NTU

International Conference on Learning Representations (ICLR), 2021. Spotlight Presentation

Project Page | PDF | Preprint | OpenReview | Slides | Citation

This repository contains an official re-implementation of RIDE from the authors, while also supports several other works on long-tailed recognition. Further information please contact Xudong Wang and Long Lian.

This repo has RIDE on ResNet and ResNeXt. For RIDE on Swin-Transformer, see it here.


If you find our work inspiring or use our codebase in your research, please consider giving a star ⭐ and a citation.

  title={Long-tailed Recognition by Routing Diverse Distribution-Aware Experts},
  author={Wang, Xudong and Lian, Long and Miao, Zhongqi and Liu, Ziwei and Yu, Stella},
  booktitle={International Conference on Learning Representations},

Supported Methods for Long-tailed Recognition:

  • RIDE
  • Cross-Entropy (CE) Loss
  • Focal Loss
  • LDAM Loss
  • Decouple: cRT (limited support for now)
  • Decouple: tau-normalization (limited support for now)


[05/2022] We release an additional RIDE repo with Swin-Transformer. Check it out.

[04/2022] We add additional iNaturalist checkpoints with more experts and longer training in model zoo.

[04/2021] Pre-trained models are avaliable in model zoo.

[12/2020] We added an approximate GFLops counter. See usages below. We also refactored the code and fixed a few errors.

[12/2020] We have limited support on cRT and tau-norm in load_stage1 option and, please look at the code comments for instructions while we are still working on it.

[12/2020] Initial Commit. We re-implemented RIDE in this repo. LDAM/Focal/Cross-Entropy loss is also re-implemented (instruction below).

Table of contents



  • Python >= 3.7, < 3.9
  • PyTorch >= 1.6
  • tqdm (Used in
  • tensorboard >= 1.14 (for visualization)
  • pandas
  • numpy

Hardware requirements

8 GPUs with >= 11G GPU RAM are recommended. Otherwise the model with more experts may not fit in, especially on datasets with more classes (the FC layers will be large). We do not support CPU training, but CPU inference could be supported by slight modification.

Dataset Preparation

CIFAR code will download data automatically with the dataloader. We use data the same way as classifier-balancing. For ImageNet-LT and iNaturalist, please prepare data in the data directory. ImageNet-LT can be found at this link. iNaturalist data should be the 2018 version from this repo (Note that it requires you to pay to download now). The annotation can be found at here. Please put them in the same location as below:

├── cifar-100-python
│   ├── file.txt~
│   ├── meta
│   ├── test
│   └── train
├── cifar-100-python.tar.gz
├── ImageNet_LT
│   ├── ImageNet_LT_open.txt
│   ├── ImageNet_LT_test.txt
│   ├── ImageNet_LT_train.txt
│   ├── ImageNet_LT_val.txt
│   ├── test
│   ├── train
│   └── val
└── iNaturalist18
    ├── iNaturalist18_train.txt
    ├── iNaturalist18_val.txt
    └── train_val2018

How to get pretrained checkpoints

We have a model zoo available.

Training and Evaluation Instructions

Imbalanced CIFAR 100/CIFAR100-LT

RIDE Without Distill (Stage 1)
python -c "configs/config_imbalance_cifar100_ride.json" --reduce_dimension 1 --num_experts 3

Note: --reduce_dimension 1 means set reduce dimension to True. The template has an issue with bool arguments so int argument is used here. However, any non-zero value will be equivalent to bool True.

RIDE With Distill (Stage 1)
python -c "configs/config_imbalance_cifar100_distill_ride.json" --reduce_dimension 1 --num_experts 3 --distill_checkpoint path_to_checkpoint

Distillation is not required but could be performed if you'd like further improvements.

RIDE Expert Assignment Module Training (Stage 2)
python -c "configs/config_imbalance_cifar100_ride_ea.json" -r path_to_stage1_checkpoint --reduce_dimension 1 --num_experts 3

Note: different runs will result in different EA modules with different trade-off. Some modules give higher accuracy but require higher FLOps. Although the only difference is not underlying ability to classify but the "easiness to satisfy and stop". You can tune the pos_weight if you think the EA module consumes too much compute power or is using too few expert.


RIDE Without Distill (Stage 1)

ResNet 10
python -c "configs/config_imagenet_lt_resnet10_ride.json" --reduce_dimension 1 --num_experts 3
ResNet 50
python -c "configs/config_imagenet_lt_resnet50_ride.json" --reduce_dimension 1 --num_experts 3
ResNeXt 50
python -c "configs/config_imagenet_lt_resnext50_ride.json" --reduce_dimension 1 --num_experts 3

RIDE With Distill (Stage 1)

ResNet 10
python -c "configs/config_imagenet_lt_resnet10_distill_ride.json" --reduce_dimension 1 --num_experts 3 --distill_checkpoint path_to_checkpoint
ResNet 50
python -c "configs/config_imagenet_lt_resnet50_distill_ride.json" --reduce_dimension 1 --num_experts 3 --distill_checkpoint path_to_checkpoint
ResNeXt 50
python -c "configs/config_imagenet_lt_resnext50_distill_ride.json" --reduce_dimension 1 --num_experts 3 --distill_checkpoint path_to_checkpoint

RIDE Expert Assignment Module Training (Stage 2)

ResNet 10
python -c "configs/config_imagenet_lt_resnet10_ride_ea.json" -r path_to_stage1_checkpoint --reduce_dimension 1 --num_experts 3
ResNet 50
python -c "configs/config_imagenet_lt_resnet50_ride_ea.json" -r path_to_stage1_checkpoint --reduce_dimension 1 --num_experts 3
ResNeXt 50
python -c "configs/config_imagenet_lt_resnext50_ride_ea.json" -r path_to_stage1_checkpoint --reduce_dimension 1 --num_experts 3


RIDE Without Distill (Stage 1)

python -c "configs/config_iNaturalist_resnet50_ride.json" --reduce_dimension 1 --num_experts 3

RIDE With Distill (Stage 1)

python -c "configs/config_iNaturalist_resnet50_distill_ride.json" --reduce_dimension 1 --num_experts 3 --distill_checkpoint path_to_checkpoint

RIDE Expert Assignment Module Training (Stage 2)

python -c "configs/config_iNaturalist_resnet50_ride_ea.json" -r path_to_stage1_checkpoint --reduce_dimension 1 --num_experts 3

Using Other Methods with RIDE

  • Focal Loss: switch the loss to Focal Loss
  • Cross Entropy: switch the loss to Cross Entropy Loss


To test a checkpoint, please put it with the corresponding config file.

python -r path_to_checkpoint

Please see the pytorch template that we use for additional more general usages of this project (e.g. loading from a checkpoint, etc.).

GFLops calculation

We provide an experimental support for approximate GFLops calculation. Please open an issue if you encounter any problem or meet inconsistency in GFLops.

You need to install thop package first. Then, according to your model, run python -m utils.gflops (args) in the project directory.

Examples and explanations

Use python -m utils.gflops to see the documents as well as explanations for this calculator.

python -m utils.gflops ResNeXt50Model 0 --num_experts 3 --reduce_dim True --use_norm False

To change model, switch ResNeXt50Model to the ones used in your config. use_norm comes with LDAM-based methods (including RIDE). reduce_dim is used in default RIDE models. The 0 in the command line indicates the dataset.

All supported datasets:

  • 0: ImageNet-LT
  • 1: iNaturalist
  • 2: Imbalance CIFAR 100
python -m utils.gflops ResNet50Model 1 --num_experts 3 --reduce_dim True --use_norm True
Imbalance CIFAR 100
python -m utils.gflops ResNet32Model 2 --num_experts 3 --reduce_dim True --use_norm True
Special circumstances: calculate the approximate GFLops in models with expert assignment module

We provide a ea_percentage for specifying the percentage of data that pass each expert. Note that you need to switch to the EA model as well since you actually use EA model instead of the original model in training and inference.

An example:

python -m utils.gflops ResNet32EAModel 2 --num_experts 3 --reduce_dim True --use_norm True --ea_percentage 40.99,9.47,49.54


See FAQ.

How to get support from us?

If you have any general questions, feel free to email us at longlian at and xdwang at If you have code or implementation-related questions, please feel free to send emails to us or open an issue in this codebase (We recommend that you open an issue in this codebase, because your questions may help others).

Pytorch template

This is a project based on this pytorch template. The readme of the template explains its functionality, although we try to list most frequently used ones in this readme.


This project is licensed under the MIT License. See LICENSE for more details. The parts described below follow their original license.


This is a project based on this pytorch template. The pytorch template is inspired by the project Tensorflow-Project-Template by Mahmoud Gemy

The ResNet and ResNeXt in fb_resnets are based on from Classifier-Balancing/Decouple. The ResNet in ldam_drw_resnets/LDAM loss/CIFAR-LT are based on LDAM-DRW. KD implementation takes references from CRD/RepDistiller.


[ICLR 2021 Spotlight] Code release for "Long-tailed Recognition by Routing Diverse Distribution-Aware Experts."







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