Improving Calibration for Long-Tailed Recognition

Authors: Zhisheng Zhong, Jiequan Cui, Shu Liu, Jiaya Jia

Introduction: This repository provides an implementation for the CVPR 2021 paper: "Improving Calibration for Long-Tailed Recognition" based on LDAM-DRW and Decoupling models. Our study shows, because of the extreme imbalanced composition ratio of each class, networks trained on long-tailed datasets are more miscalibrated and over-confident. MiSLAS is a simple, and efficient two-stage framework for long-tailed recognition, which greatly improves recognition accuracy and markedly relieves over-confidence simultaneously.



  • Python 3.7
  • torchvision 0.4.0
  • Pytorch 1.2.0
  • yacs 0.1.8

Virtual Environment

conda create -n MiSLAS python==3.7
source activate MiSLAS

Install MiSLAS

git clone https://github.com/Jia-Research-Lab/MiSLAS.git
pip install -r requirements.txt

Dataset Preparation

Change the data_path in config/*/*.yaml accordingly.



To train a model for Stage-1 with mixup, run:

(one GPU for CIFAR-10-LT & CIFAR-100-LT, four GPUs for ImageNet-LT, iNaturalist 2018, and Places-LT)

python train_stage1.py --cfg ./config/DATASETNAME/DATASETNAME_ARCH_stage1_mixup.yaml

DATASETNAME can be selected from cifar10, cifar100, imagenet, ina2018, and places.

ARCH can be resnet32 for cifar10/100, resnet50/101/152 for imagenet, resnet50 for ina2018, and resnet152 for places, respectively.


To train a model for Stage-2 with one GPU (all the above datasets), run:

python train_stage2.py --cfg ./config/DATASETNAME/DATASETNAME_ARCH_stage2_mislas.yaml resume /path/to/checkpoint/stage1

The saved folder (including logs and checkpoints) is organized as follows.

├── saved
│   ├── modelname_date
│   │   ├── ckps
│   │   │   ├── current.pth.tar
│   │   │   └── model_best.pth.tar
│   │   └── logs
│   │       └── modelname.txt
│   ...   


To evaluate a trained model, run:

python eval.py --cfg ./config/DATASETNAME/DATASETNAME_ARCH_stage1_mixup.yaml  resume /path/to/checkpoint/stage1
python eval.py --cfg ./config/DATASETNAME/DATASETNAME_ARCH_stage2_mislas.yaml resume /path/to/checkpoint/stage2

Results and Models

1) CIFAR-10-LT and CIFAR-100-LT

  • Stage-1 (mixup):
Dataset Top-1 Accuracy ECE (15 bins) Model
CIFAR-10-LT IF=10 87.6% 11.9% link
CIFAR-10-LT IF=50 78.1% 2.49% link
CIFAR-10-LT IF=100 72.8% 2.14% link
CIFAR-100-LT IF=10 59.1% 5.24% link
CIFAR-100-LT IF=50 45.4% 4.33% link
CIFAR-100-LT IF=100 39.5% 8.82% link
  • Stage-2 (MiSLAS):
Dataset Top-1 Accuracy ECE (15 bins) Model
CIFAR-10-LT IF=10 90.0% 1.20% link
CIFAR-10-LT IF=50 85.7% 2.01% link
CIFAR-10-LT IF=100 82.5% 3.66% link
CIFAR-100-LT IF=10 63.2% 1.73% link
CIFAR-100-LT IF=50 52.3% 2.47% link
CIFAR-100-LT IF=100 47.0% 4.83% link

Note: To obtain better performance, we highly recommend changing the weight decay 2e-4 to 5e-4 on CIFAR-LT.

2) Large-scale Datasets

  • Stage-1 (mixup):
Dataset Arch Top-1 Accuracy ECE (15 bins) Model
ImageNet-LT ResNet-50 45.5% 7.98% link
iNa'2018 ResNet-50 66.9% 5.37% link
Places-LT ResNet-152 29.4% 16.7% link
  • Stage-2 (MiSLAS):
Dataset Arch Top-1 Accuracy ECE (15 bins) Model
ImageNet-LT ResNet-50 52.7% 1.78% link
iNa'2018 ResNet-50 71.6% 7.67% link
Places-LT ResNet-152 40.4% 3.41% link


Please consider citing MiSLAS in your publications if it helps your research. :)

    title={Improving Calibration for Long-Tailed Recognition},
    author={Zhisheng Zhong, Jiequan Cui, Shu Liu, and Jiaya Jia},
    booktitle={IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},


If you have any questions about our work, feel free to contact us through email (Zhisheng Zhong: [email protected]) or Github issues.