A post was merged into an existing topic: Lesson 3 - Non-beginner discussion
On the validation set, we center crop the image if it’s ratio isn’t in the range (to the minmum or maximum value) then resize. Link is:
Documentation of RandomResizedCrop
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At each epoch you get a new transformation.
Are there pre-packaged transformations that are not affine? Things like lightness, hue, sharpness…
what’s the best rule of thumb with regards to how large a batch to feed at a time relative to gpu size?
Is there any significance to the size of the crop to be 224x224 ?
There is lightning and contrast transforms, randomly erasing a part of the image, perspective warping. Look at the docs on vision.augment for the whole list.
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Either inherit new class from Transform or create new function with @Transform decorator.
Jeremy runs through these in source code walkthru videos.
If the data set in imbalanced, can I choose to augment only the under represented class? or choose a particular type of transformation that suits a class best.
Usually, whatever fits on your GPU is fine.
So the transformation is the same to each image per epoch? Just wanna make sure I understand.
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How do you pick what augmentations to do? Do certain augmentations work better than others for a task? Does it make sense to schedule augmentations during training?
training will be faster and you can iterate on your model
Why 224 not say 480 , 720 which is close to capture sizes ?
No, it’s a random transformation for each image at each epoch.
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This comes from trying different augmentations and checking validation accuracy. There is no definite way AFAIK to pick augmentations and be sure they’ll work. It usually is trial and error.
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Too large, and memory consumption will be too high. Also, traditionally, 224x224 has been used in other deep learning models.
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So the image_tfms are not gpu accelerated?
For all of these questions, the answer is “Try it!”. It’s certainly problem-dependent and there is no definitive answer.
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