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The code of "Revisiting Local Descriptor based Image-to-Class Measure for Few-shot Learning", CVPR 2019.
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README.md

DN4 in PyTorch

We provide a PyTorch implementation of DN4 for few-shot learning. If you use this code for your research, please cite:

Revisiting Local Descriptor based Image-to-Class Measure for Few-shot Learning.
Wenbin Li, Lei Wang, Jinglin Xu, Jing Huo, Yang Gao and Jiebo Luo. In CVPR 2019.

Prerequisites

  • Linux
  • Python 3
  • Pytorch 0.4 or 1.0
  • GPU + CUDA CuDNN
  • pillow, torchvision, scipy, numpy

Getting Started

Installation

  • Clone this repo:
git clone https://github.com/WenbinLee/DN4.git
cd DN4
  • Install PyTorch 1.0 and other dependencies.

Datasets

miniImageNet Few-shot Classification

  • Train a 5-way 1-shot model based on Conv64F or ResNet256F:
python DN4_Train_5way1shot.py --dataset_dir ./datasets/miniImageNet --data_name miniImageNet
or
python DN4_Train_5way1shot_Resnet.py --dataset_dir ./datasets/miniImageNet --data_name miniImageNet
  • Test the model (specify the dataset_dir, basemodel, and data_name first):
python DN4_Test_5way1shot.py --resume ./results/DN4_miniImageNet_Conv64F_5Way_1Shot_K3/model_best.pth.tar --basemodel Conv64F
or
python DN4_Test_5way1shot.py --resume ./results/DN4_miniImageNet_ResNet256F_5Way_1Shot_K3/model_best.pth.tar --basemodel ResNet256F
  • The results on the miniImageNet dataset (If you set neighbor_k as 1, you may get better results in some cases):

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_prepare_csv.py
python ./dataset/StanfordDog_prepare_csv.py
  • Train a 5-way 1-shot model:
python DN4_Train_5way1shot.py --dataset_dir ./datasets/StanfordDog --data_name StanfordDog
  • Test the model (specify the dataset_dir and data_name first):
python DN4_Test_5way1shot.py --resume ./results/DN4_StanfordDog_Conv64F_5_Way_1_Shot/model_best.pth.tar --basemodel Conv64F
  • The results on the fine-grained datasets:

Citation

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

@inproceedings{li2019DN4,
  title={Revisiting Local Descriptor based Image-to-Class Measure for Few-shot Learning},
  author={Li, Wenbin and Wang, Lei and Xu, Jinglin and Huo, Jing and Gao Yang and Luo, Jiebo},
  booktitle={CVPR},
  year={2019}
}
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