kostas1515 / iif

Inverse Image Frequency for long-tailed image recognition. Accepted in Transactions on Image Processing - Arxiv version available at: https://arxiv.org/abs/2209.04861

Inverse Image Frequence for Long-tailed Image Recognition

Abstract The long-tailed distribution is a common phenomenon in the real world. Extracted large scale image datasets inevitably demonstrate the long-tailed property and models trained with imbalanced data can obtain high performance for the over-represented categories, but struggle for the under-represented categories, leading to biased predictions and performance degradation. To address this challenge, we propose a novel de-biasing method named Inverse Image Frequency (IIF). IIF is a multiplicative margin adjustment transformation of the logits in the classification layer of a convolutional neural network. Our method achieves stronger performance than similar works and it is especially useful for downstream tasks such as long-tailed instance segmentation as it produces fewer false positive detections. Our extensive experiments show that IIF surpasses the state of the art on many long-tailed benchmarks such as ImageNet-LT, CIFAR-LT, Places-LT and LVIS, reaching 55.8 top-1 accuracy with ResNet50 on ImageNet-LT and 26.3 segmentation AP with MaskRCNN ResNet50 on LVIS.

Progress

• Training code.
• Evaluation code.
• LVIS v1.0, ImageNet-LT, Places-LT datasets.
• Provide classification checkpoint models.
• Provide instance segmentation checkpoint models.

Tested with

• python==3.8.12
• torch==1.7.1
• torchvision==0.8.2
• mmdet==2.15.1
• lvis
• Tested on CUDA 10.1,10.0

Please Note that there is a reproducibility issue when using CUDA 10.2, as it drops classification performance by ~5%. For this reason please use either cuda 10.1 or cuda 10.0. Other versions are not tested.

Getting Started

Create a virtual environment

conda create --name mmdet pytorch=1.7.1 -y
conda activate mmdet

1. Install dependency packages

conda install torchvision -y
conda install pandas scipy -y
conda install opencv -y
pip install catalyst
pip install imgaug
pip install randaugment

2. Install MMDetection

pip install openmim
mim install mmdet==2.15.1

3. Clone this repo

git clone https://github.com/kostas1515/iif.git
cd iif

Datasets

For COCO and LVIS datasets:

1. Create data directory, download COCO 2017 datasets at https://cocodataset.org/#download (2017 Train images [118K/18GB], 2017 Val images [5K/1GB], 2017 Train/Val annotations [241MB]) and extract the zip files:

mkdir data
cd data
wget http://images.cocodataset.org/zips/train2017.zip
wget http://images.cocodataset.org/zips/val2017.zip

#download and unzip LVIS annotations
wget https://s3-us-west-2.amazonaws.com/dl.fbaipublicfiles.com/LVIS/lvis_v1_train.json.zip
wget https://s3-us-west-2.amazonaws.com/dl.fbaipublicfiles.com/LVIS/lvis_v1_val.json.zip

2. modify mmdetection/configs/base/datasets/lvis_v1_instance.py and make sure data_root variable points to the above data directory, e.g., data_root= "<user_path>"

For ImageNet and Places-LT:

1. Download the ImageNet_2014 and Places_365.

Citation

 @article{alexandridis2023inverse,
  title={Inverse Image Frequency for Long-tailed Image Recognition},
  author={Alexandridis, Konstantinos Panagiotis and Luo, Shan and Nguyen, Anh and Deng, Jiankang and Zafeiriou, Stefanos},
  journal={IEEE Transactions on Image Processing},
  year={2023},
  publisher={IEEE}
}

Acknowledgements

This code uses the mmdet framework for instance segmentation. For classification, it uses MiSLAS and LDAM. Thank you for your wonderfull work!