CHEF
Cross-domain few-shot learning by representation fusion
Download and prepare data
miniImagenet
Download the miniImagenet data from
here.
We use the data split proposed by
Ravi & Larochelle.
Create the folders images_train, images_val, images_test and place
the respective files in them, as well as a folder images_trainval that
must contain all images from images_train and images_val.
tieredImagenet
Download the tiereImagenet data from
here.
Extract it and create a folder trainval containing all images
from the folders train and val.
Set up horizontal data splits
Run python3 make_miniImagenet_hsplit.py and
python3 make_tieredImagenet_hsplit.py to set up the horizontal
data splits for pretraining.
Cross-domain data
Follow the instructions in this repo to acquire and set up the cross-domain data sets.
Pre-training
Run python3 pretrain.py config/pretrain_{res10,res12,conv64}_{tier,mini}.json.
Testing
To test the pre-trained ResNet-10 on the four cross-domain data sets run
python3 xdom_res10.py config/xdom_res10.json --dataset {isic,cropdisease,eurosat,chest}.
To test the Imagenet-pre-trained ResNet-18 from PyTorch on the four
cross-domain data sets run
python3 xdom_res18.py config/xdom_res18.json --dataset {isic,cropdisease,eurosat,chest}.
Without the --dataset option, the model will be run on the miniImagenet test
set by default.
To test the pre-trained ResNet-12 and Conv-4 on the miniImagenet and
tieredImagenet test sets run
python3 test.py config/{res12,conv64}_{mini,tier}_{1,5}shot.json.