Image Deformation Meta-Networks for One-Shot Learning

A PyTorch implementation of "Image Deformation Meta-Networks for One-Shot Learning"(CVPR 2019 Oral).

Image Deformation Meta-Networks for One-Shot Learning,
Zitian Chen, Yanwei Fu, Yu-Xiong Wang, Lin Ma, Wei Liu, Martial Hebert

Installation

python=2.7
pytorch=0.4.0

Datasets

The data split is from Semantic Feature Augmentation in Few-shot Learning

Please put the data in:
/home/yourusername/data/miniImagenet

The images are put in 
.../miniImagenet/images
such as:miniImagenet\images\n0153282900000006.jpg
We provide the data split in ./datasplit/,please put them at 
.../miniImagenet/train.csv
.../miniImagenet/test.csv
.../miniImagenet/val.csv

Train & Test

Notice that we train the model on 4 Titan X. 42000MB GPU memory is required or may cause CUDA out of memory.

# First, we fix the deformation sub-network and train the embedding sub-network with randomly deformed images

# We provide softRandom.t7 as the embedding sub-network
# if you want to train your own, run python classification.py --tensorname yournetworkname


# Then, we fix the embedding sub-network and learn the deformation sub-network 

CUDA_VISIBLE_DEVICES=0,1,2,3 python onlyBasetwoLoss.py --network softRandom --shots 5 --augnum 5 --fixCls 1 --tensorname metaNet_5shot --chooseNum 30 

# If you want to further improve, then fix one sub-network and iteratively train the other. 

# update cls
CUDA_VISIBLE_DEVICES=0,1,2,3 python onlyBasetwoLoss.py --network softRandom --shots 5 --augnum 5 --fixCls 0 --fixAttention 1 --tensorname metaNet_5shot_round2 --chooseNum 30 --GNet metaNet_5shot 

We also provide our model: metaNet_1shot.t7 and metaNet_5shot.t7 in ./models

You can use --GNet metaNet_1shot to load the model.

License

IDeMe-Net is released under the MIT License (refer to the LICENSE file for details).

Citation

If you find this project useful for your research, please use the following BibTeX entry.