Comments (2)
Thanks for your interest in our work!
I currently do not have access to the original config for this training-from-scratch experiments. Since its performance is limited, we did not release its config.
I remember the config should be like, in train_meta_mini.yaml:
- delete the row "load_encoder"
- set "ep_per_batch" to 8 (or 16)
- set "train_batches" to 300
- set "max_epoch" to 180, set "lr: 0.1" and add "milestones: [90] (or [90, 150])" in "optimizer_args"
The parameters above may not be accurate. The general idea is to train the model with initial lr 0.1 and decay it when validation loss gets plateau. You may adjust these parameters according to the training plot.
from few-shot-meta-baseline.
OK, I will try your suggestion. Thanks for your reply!
from few-shot-meta-baseline.
Related Issues (20)
- cite
- Question about the meta-test. HOT 2
- Thank you for your attention HOT 5
- The expected results are not achieved on the tiered-imagenet HOT 9
- 训练自己的数据集 HOT 2
- How did you get the encoder that you shared in the git hub project? HOT 1
- An error occurred during operation
- classifier related to baseline in Closer look at few-shot leawrning
- some training problems using "max_epoch=100"
- 您好,meta-learning会导致新类别泛化能力下降,那么为什么还会提升classifier-baseline的性能呢? HOT 2
- partition of dataset
- The question of distance selection
- how to run on CPU?
- 关于encoder训练的问题
- How do I know what the real category of tieredImagenet is?
- EOFError: Ran out of input HOT 1
- tval_dataset uses the test dataset?
- Hello, could you please upload these three data sets? I think they are pickle files, thank you very much。
- I think the URL of miniImagenet is outdated, could you please upload it again?
- I think the URL of miniImagenet is outdated, could you please upload it again? Thank you very much.
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from few-shot-meta-baseline.