Dog Breed Identification challenge

Part 1 (2018)
42

@jamesrequa That worked perfectly, thanks a lot :smiley:

1 Like
43

TTA returns a tuple. The 2nd item are the y values.

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44

@jeremy this makes sense for predicting on the validation set but for the test set we don’t have any y values so in that case shouldn’t learn.TTA(is_test=True) just return the predictions?

If you agree I could submit a PR for it. :slight_smile:

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45

IIRC it just returns zeros for the y values for the test set - I figured it’s more convenient for it to be consistent, so you don’t have to write different code for different datasets.

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46

How do I check the most correct predictions for each breed while analysing the results?

In the dogs vs cats it was a classification problem - and I could call this line as I knew 1 was a dog.

plot_val_with_title(most_by_correct(1, True), "Most correct dogs")

But wondering how to check when having multiple outputs - and also how to display the title for each.

Thanks.

47

@jeremy Thanks, got it! Actually I saw bunch of zeros as 2nd item, now totally make sense :slight_smile:

48

You’ll need to write that code yourself - it would be a great exercise, actually. Let us know if you try and get stuck!

1 Like
49

Maybe a silly question. Why do we need to take np.exp of the prediction?

50

That gives us the probabilities.

51

In the below code, why are we choosing 300 as the default size value to check if condition?

def get_data(sz, bs):
    tfms = tfms_from_model(arch, sz, aug_tfms=transforms_side_on, max_zoom = 1.1)
    data = ImageClassifierData.from_csv(PATH, 'train', f'{PATH}labels.csv', 
                                    test_name ='test', val_idxs=val_idxs, suffix='.jpg', tfms=tfms, bs=bs)
    return data if sz>300 else data.resize(340, 'tmp')
3 Likes
52

Most pytorch models return the log of the probabilities.

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53

Great question. Since we have max_zoom=1.1, I figured we should ensure our images are at release sz*1.1. And I figured resizing them to 340x340 would save plenty of time, and leave plenty of room to experiment.

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54

I created this Kernel so I could share some code here:

https://www.kaggle.com/kevinbird15/breaking-data-into-train-and-valid

This really helped me quickly break my train folder into train and valid and also broke everything into folders.

It didn’t really work great as a Kernel, but I didn’t know where else to share it. Hopefully it isn’t too annoying in their list of Kernels.

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55

If you use from_csv like we did in the last lesson you can skip all that :slight_smile:

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56

Hi @jeremy, just qq - what method allows not to use validation set at all?

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57

Any method should allow that. Just don’t set val_name in from_paths or don’t set val_idxs in from_csv.

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58

in that case would you explicitly set it to val_name=""since it has a default value?

59

Set it to None I think. Not sure I’ve actually tried it, so just yell if it doesn’t work.

60

Has anyone uploaded to kaggle from crestle ? I am not sure how to get hold of the .csv file generated to upload it manually on kaggle site.

61

You can use kaggle-cli to upload, or use FileLink like so: https://stackoverflow.com/questions/24437661/retrieving-files-from-remote-ipython-notebook-server

1 Like