This repository is the official PyTorch implementation of ICCV-21 paper ACE: Ally Complementary Experts for Solving Long-Tailed Recognition in One-Shot.
To install the environment.
conda env create -f environment.yml conda activate ACE git clone https://github.com/NVIDIA/apex cd apex pip install -v --disable-pip-version-check --no-cache-dir --global-option="--cpp_ext" --global-option="--cuda_ext" ./
The annotation of a dataset is a dict consisting of two field: annotations and num_classes.
The field annotations is a list of dict with
image_id, fpath, im_height, im_width and category_id.
Here is an example.
{
'annotations': [
{
'image_id': 1,
'fpath': '/data/iNat18/images/train_val2018/Plantae/7477/3b60c9486db1d2ee875f11a669fbde4a.jpg',
'im_height': 600,
'im_width': 800,
'category_id': 7477
},
...
]
'num_classes': 8142
}
#bash data_parallel_train.sh configuration_file_path GPU_indexes
bash data_parallel_train.sh configs/cifar100_im100.yaml 0,1 #python valid.py configuration_file_path
python valid.py configs/cifar100_im100.yamlThis project is developed based on Bag of tricks @AAAI-21, thanks for their works!
@inproceedings{cai2021ace,
title={ACE: Ally Complementary Experts for Solving Long-Tailed Recognition in One-Shot},
author={Cai, Jiarui and Wang, Yizhou and Hwang, Jenq-Neng},
booktitle={Proceedings of the IEEE/CVF International Conference on Computer Vision},
pages={112--121},
year={2021}
}