https://arxiv.org/abs/2202.01186
For experimental evaluation several ProtoNet models were trained on CUB200-2011, CIFAR-FS or mini-ImageNet. Pre-trained model weights are available here The training process can be reproduced by obtaining a copy of the dataset and running:
cd code
python protonet/train.py --dataset DATASET --dataset_root PATH_TO_IMAGES --splits_root PATH_TO_SPLITS --experiment_root PATH_TO_CHECKPOINT --classes_per_it_tr CL_PI_TR --iterations IT --num_support_tr N_SUP_TR --num_support_val N_SUP_VAL --num_query_tr N_QUE_TR --epochs N_EPOCHS --cuda
Options:
- DATASET: either 'cub200', 'cifar-fs' or 'mini-imagenet'
- PATH_TO_IMAGES: path to the folder, containing images
- PATH_TO_SPLITS: path to the folder, containing splits to classes in either .txt or .csv format
- PATH_TO_CHECKPOINT: path to the folder where the results of training would be saved
- CL_PI_TR: number of classes per batch for training, standard number is 5
- IT: number of iterations per epoch, standard number is 1000
- N_SUP_TR: number of support examples per class in batch for training (1 for 1-shot and 5 for 5-shot)
- N_SUP_VAL: number of support examples per class in batch for validation
- N_QUE_TR: number of query examples per class in batch for training, default is 15
- N_QUE_VAL: number of query examples per class in batch for validation, default is 15
For testing certified accuracy of smoothed models on test set, update certification.sh with pathes to datasets and trained models as well as certify_example.sh with the desired dataset setting and then run:
bash certify_example.sh
To visualize the results just run code/visualize.py