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CovaMNet in PyTorch

We provide a PyTorch implementation of CovaMNet for few-shot learning. The code was written by Wenbin Li [Homepage].

If you use this code for your research, please cite:

Distribution Consistency based Covariance Metric Networks for Few-shot Learning.
Wenbin Li, Jinglin Xu, Jing Huo, Lei Wang, Yang Gao and Jiebo Luo. In AAAI 2019.

Prerequisites

  • Linux
  • Python 3
  • Pytorch 0.4
  • GPU + CUDA CuDNN

Getting Started

Installation

  • Clone this repo:
git clone https://github.com/WenbinLee/CovaMNet
cd CovaMNet
  • Install PyTorch 0.4 and other dependencies (e.g., torchvision).

Datasets

miniImageNet Few-shot Classification

  • Train a 5-way 1-shot model:
python CovaMNet_Train_5way1shot.py --dataset_dir ./datasets/miniImageNet --data_name miniImageNet
  • Test the model (specify the dataset_dir and data_name first):
python CovaMNet_Test_5way1shot.py --resume ./results/CovaMNet_miniImageNet_Conv64_5_Way_1_Shot/model_best.pth.tar
  • The results on the miniImageNet dataset:

Fine-grained Few-shot Classification

  • Data prepocessing (e.g., StanfordDog)
  • Specify the path of the dataset and the saving path.
  • Run the preprocessing script.
#!./dataset/StanfordDog/StanfordDog_prepare_csv.py
python ./dataset/StanfordDog/StanfordDog_prepare_csv.py
  • Train a 5-way 1-shot model:
python CovaMNet_Train_5way1shot.py --dataset_dir ./datasets/StanfordDog --data_name StanfordDog
  • Test the model (specify the dataset_dir and data_name first):
python CovaMNet_Test_5way1shot.py --resume ./results/CovaMNet_StanfordDog_Conv64_5_Way_1_Shot/model_best.pth.tar
  • The results on the fine-grained datasets:

Citation

If you use this code for your research, please cite our paper.

@inproceedings{li2019CovaMNet,
  title={Distribution Consistency based Covariance Metric Networks for Few-shot Learning},
  author={Li, Wenbin and Xu, Jinglin and Huo, Jing and Wang, Lei and Gao Yang and Luo, Jiebo},
  booktitle={AAAI},
  year={2019}
}

About

The Pytorch code of "Distribution Consistency based Covariance Metric Networks for Few-shot Learning", AAAI 2019.

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