1.--dataset miniImagenet --method metatrain --train_aug --test maml5_resnet
use this command, the accuracy is 92%
--dataset miniImagenet --model na --method metatrain --train_aug --test maml5_ifsl_resnet
use this command, the accuracy is 89.33%
This is not consistent with the results in the paper. In particular, ifsl does not improve the accuracy of maml
2.--method metatrain --train_aug --test maml5_ifsl_resnet_tiered
use this command, the following error occurs:
size mismatch for fc.weight: copying a param with shape torch.Size([64, 512]) from checkpoint, the shape in current model is torch.Size([351, 512]).
size mismatch for fc.bias: copying a param with shape torch.Size([64]) from checkpoint, the shape in current model is torch.Size([351]).
The parameters of the pre-training model are the same as those of miniImagenet.
3.In the MateTrain.py, I find some commands about new_test.py, but I couldn't find such a document.
1.--dataset miniImagenet --method metatrain --train_aug --test maml5_resnet use this command, the accuracy is 92%
--dataset miniImagenet --model na --method metatrain --train_aug --test maml5_ifsl_resnet use this command, the accuracy is 89.33%
This is not consistent with the results in the paper. In particular, ifsl does not improve the accuracy of maml
2.--method metatrain --train_aug --test maml5_ifsl_resnet_tiered use this command, the following error occurs: size mismatch for fc.weight: copying a param with shape torch.Size([64, 512]) from checkpoint, the shape in current model is torch.Size([351, 512]). size mismatch for fc.bias: copying a param with shape torch.Size([64]) from checkpoint, the shape in current model is torch.Size([351]).
The parameters of the pre-training model are the same as those of miniImagenet.
3.In the MateTrain.py, I find some commands about new_test.py, but I couldn't find such a document.