Comments (7)
@ehsanmok ,hi, however ,in the
https://github.com/floodsung/LearningToCompare_FSL/blob/master/miniimagenet/miniimagenet_train_few_shot.py#L15
you just use the "task_generator_test", not the "task_generator"……
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It's based on meta validation set: See 1, 2, 3
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Right! not a good code. It's the third mistake along with not using model.eval() and using the same normalization for omniglot and mini-imagenet!
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However, it's done correctly for one shot here. Based on the copy pasting attitude of the code maybe it was changed at the time of training and when released the code wasn't carefully done!
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@ehsanmok Hello! I think that although the code uses "task_generator_test" instead of "task_generator" in miniimagenet_train_few_shot.py, it doesn't influence the result of model training because "metatest_folders" is only used for monitoring generalization performance. It doesn't participate in the process of model training.
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I would like to ask, is there a problem with the model selection based on omniglot? Should be based on the accuracy of training to choose it
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When selecting a model, the test data is unknown, so the accuracy of the test cannot be used to select the model.
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Related Issues (20)
- for text classification?
- Question about training and testing using the same dataset. HOT 2
- How can I find the task_generator_test.py ?
- RuntimeError: Expected object of scalar type Long but got scalar type Int for argument #3 'index' HOT 2
- RuntimeError: Attempting to deserialize object on CUDA device 1 but torch.cuda.device_count() is 1. Please use torch.load with map_location to map your storages to an existing device. HOT 1
- Question about mini imagenet dataset
- Corrected depreciated functions and tested using ipython
- How to ensure each category in support set is the same as that in query set?
- RuntimeError: invalid argument 0: Sizes of tensors must match except in dimension 2. Got 1 and 50 in dimension 0 at /pytorch/torch/lib/THC/generic/THCTensorMath.cu:111
- IndexError: scatter_(): Expected dtype int64 for index. HOT 3
- sample larger than population"
- When running omniglot_train_one_shot.Time costs。 HOT 1
- RuntimeError: shape '[-1, 128, 19, 19]' is invalid for input of size 46656000 on miniimagenet code
- Question about calculating accuracy
- Can I use contrastive loss here?
- Train on my own dataset
- Test on different dataset, not miniimagenet, not omniglot
- 💪 Reproduce LearningToCompare_FSL environment on Ubuntu 16.04 CUDA 8.0
- self.train_labels = [labels[self.get_class(x)] for x in self.train_roots] KeyError: '..\\datas\\omniglot_28x28' HOT 4
- Data Leakage!
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