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A Visualizer for PyTorch Image Transformations

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Tldr; Check out the app here.

Image augmentation is a common technique used when training computer vision models in order to generate artificial training data by transforming in your actual training data, for example, random rotations and shifts.

Alt TextWe created 4 new images of cats!

However these augmentations can often be a source of subtle bugs. For example here is an typical PyTorch transform pipeline:

from torchvision import transforms

transform = transforms.Compose([
    transforms.RandomAffine(degrees=360, translate=(0.64, 0.98), scale=(0.81, 2.85), shear=(0.1, 0.5), fill=0, interpolation=InterpolationMode.NEAREST),
    transforms.ColorJitter(brightness=0.62, contrast=0.3, saturation=0.44, hue=0.24),
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Do you see anything obviously wrong with it?

Well, let’s see what happens when we actually try to apply these transformations to our cat.

Alt TextHalf of these don’t even look like cats!

As you can see the transformations were not properly tuned and resulted in a significant number of images being completely unrecognizable. If we train the model on this augmented dataset, it’s no longer learning what a cat looks like.
These sorts of bugs are tricky because no errors will be raised. Instead the result would be that the model will not perform as well on the un-augmented test dataset as it could have.

Introducing a PyTorch transforms visualizer

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You can use this tool to develop and sanity check your transforms on actual images before using them in a training script. It supports all the transforms provided in the torchvision.transforms package.

Check it out here!

Discussion (4)

dangerous profile image
Tommy DANGerous

This is super helpful for generating additional training data!

nathanielthemage profile image

I love that you can try out the image transformations on a website!

thomaschung408 profile image
Tom Chungry

Very cool!

wangxiaoyou1993 profile image

This visualization tool makes parameter tuning for image transformation easier!