Hot answers tagged


More generally, this would be indicative of a problem. In your context, where you're confident that the test set is representative of the intended production setting, and the lower scores on the training set may be due to the augmentation, I think you're probably fine to proceed. To be a little more confident, I'd want to evaluate the hypothesis that the ...


In other words, your model doesn't learn very well on the training data, but depite that, it does good predictions on test data, right? The short answer is no, because there is a big risk of biased results in production. The long answer is you have to know whether the test data is representative enough of the production data or not. Do you also use the same ...


It goes to PosixPath('/root/.fastai/data/oxford-iiit-pet') so you won't see it on the data pane.


One way face recognition is done is with one-shot learning and siamese networks. You only gather one example of the face you want to recognize. You then train two CNNs with shared parameters that are able to encode one image each. Then you feed both these encodings to something that can measure the similarity between them and compare that to the ground truth ...


How do I download these many different images? Since it's pretty hard to manually download the pictures of all, is there a way to automate it? First of all, you need to get an idea of how your classifier will be, will it classify variable size images or fixed size images? Will it receive only one face as an input or more than one? Will it classify black and ...


Those parameters are taken care of by **kwargs in the function definition. You can look how this is dealt with by the code in the fastai repo and looking for **kwargs in the function definition. Some explanations about how this works can be found on SO:

Only top voted, non community-wiki answers of a minimum length are eligible