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.
- Linux
- Python 3
- Pytorch 0.4 or 1.0
- GPU + CUDA CuDNN
- pillow, torchvision, scipy, numpy
- Clone this repo:
git clone https://github.com/WenbinLee/DN4
cd DN4- Install PyTorch 1.0 and other dependencies.
- miniImageNet.
- StanfordDog.
- StanfordCar.
- CUB-200.
Thanks Victor Garcia for providing the miniImageNet dataset. In our paper, we just used the CUB-200 dataset. In fact, there is a newer revision of this dataset with more images, see Caltech-UCSD Birds-200-2011. Note, if you use these datasets, please cite the corresponding papers.
- 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):
- 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:
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}
}