question about dataset

[changykang]

hi,i download the dataset from the link, but they donot include lable files, can you help me solve this problem. thank you.

[yaoyao-liu]

Thanks for your interest in our work.

You don't need the label files. The images for one class are saved in a corresponding folder.
You may use the provided dataloader to generate episodes for few-shot learning.

[changykang]

thank you solve my problem,and can i directly run the run_experiment.py in tensorflow?

[yaoyao-liu]

Yes. After you install all the requirements and put the data in the proper directory, you may run the command line following the README.md file.

[changykang]

ok, thank you reply, when i install tensorflow-gpu=1.3.0 under the python=2.7, it shown packagenotfounderror, can you help me.

[yaoyao-liu]

If the packages can not be directly downloaded, you may build TensorFlow 1.3.0 according to this link.

[changykang]

Thank you for solving my problem patiently

[changykang]

i have another question about the dataset, what is the dataset in the meta-train and meta-test, and how did you split the support set and query set in the miniImageNet dataset? thank you

[yaoyao-liu]

The splits of miniImageNet are available here.
There are two different splits for miniImageNet. Most papers follow the split provided by Vinyals et al.

During the meta-training phase, the support and query sets of each episode/task are randomly sampled from the training set of miniImageNet. During the meta-test phase, the support and query sets of each episode/task are randomly sampled from the test set of miniImageNet. You may easily find the generation protocol in any few-shot learning paper, e.g., MAML.

[changykang]

That is, in the large-scale phase, 64 classes are used for training; the meta-training phase is randomly sampled; the meta-teste stage uses the test data of miniimagenet. Am I right?

[yaoyao-liu]

During the pre-train phase, we train the encoder on a 64-way classification task using the meta-train set.

During the meta-train phase, we train the meta model on many 5-way small classification tasks generated from the meta-train set.

During the meta-test phase, we evaluate the model on many 5-way small classification tasks generated from the meta-test set.