Comments (4)
Thank you so much! It's really helpful for my cases.
from frn.
Hi, thanks for ur interest in our work!
Since this codebase is mainly intend for reproducing the training and evaluation of our proposed method, it might be a bit hard to adapt it to a single demo showcase.
I think you can refer to the meta_test()
function we used during evaluation. If you replaced the dataloader with yours (or replace the per-episode inp
tensor with your post-processed data tensors), it should be fine. But it should be noted that, in addition to the query images, you also need to give the few-shot support images as part of the input (more details could be found in our dataloader sampler and model meta_test functions.)
Feel free to let us know if u have more questions.
from frn.
Closing for now. Feel free to reopen if u have more questions.
from frn.
Thank you so much! It's really helpful for my cases.
hello! I have the same question as you. Can you share some details about how you solved this?
from frn.
Related Issues (16)
- what is the val acc when pre-training on the Miniimagenet dataset is finished? HOT 1
- Are drop blocks used indeed? HOT 2
- Issue on the aux loss HOT 4
- Issue on pre-training HOT 10
- Performance on CUB HOT 2
- RuntimeError: inverse_cuda: For batch 0: U(2,2) is zero, singular U. HOT 3
- Validation accuracy of pre-training on the tieredImageNet dataset HOT 4
- pretrain accuracy HOT 12
- How to change the batch_size HOT 2
- About MatchingNet HOT 1
- Issue on regularization HOT 3
- About the training process HOT 3
- Can authors open source the visualization part of the code? HOT 6
- Some data set links failed HOT 2
- Request for visualization code HOT 3
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from frn.