Different data augmentation recipes in tf.keras for image classification | Sayak Paul

[utterances-bot]

Different data augmentation recipes in tf.keras for image classification | Sayak Paul

Learn about different ways of doing data augmentation when training an image classifier in tf.keras.

https://sayak.dev/tf.keras/data_augmentation/image/2020/05/10/augmemtation-recipes.html

[abhinavsp0730]

Awesome and very useful 🎊

[sayakpaul]

Glad you liked it.

[martin-gorner]

Great article Sayak ! By the way, have you tried using the new data augmentation layers in your tf.data.Dataset pipeline ?

[sayakpaul]

Thank you so much, Martin!

Were you referring to these layers: https://www.tensorflow.org/api_docs/python/tf/keras/layers/experimental/preprocessing?

If so, then yes, I used it here.

[adroit91]

Hi Sayak, good write up.

Why would you have data augmentation as part of the network itself? Doesn't it make it tedious to skip augmentation at test time?

[sayakpaul]

There are some existing works that utilize augmentations like center crops, flips, etc. during test time. That is why.

[JonasImazon]

Hello Sayak, It was a good reading

But I have some question regarding th usage of tf.data API. When I use that approach, the data augmentation is applied only once, right? It is not applied during training time per each epoch. If that is true, how can I do it during training to apply variety to my dataset each time it changes epoch?

I really appreciate your work and thank you for you attention.

[sayakpaul]

It's applied during each epoch actually. You can verify that by adding random augmentation transformations like random size cropping and plotting the augmented images after a fixed number of steps. This would roughly replicate how images will be fed to your network during training.