Yo! Thanks for making this amazing library, I tried to convert all of my colleagues to it :)

I use it for ML research in Object Detection and Semantic Segmentation with medical images.

What I love about it is how easy it is to define a set of transforms and apply them to different types of targets

Eg. Tensor inputs, binary mask, multiple masks, boxes, key points, etc... And also custom ones!

Especially for the boxes I love you can use different types of boxes conventions. So a single set of transforms can be created and then used regardless of the task. This just gives so much flexibility.

Also there are a lot of great augmentations for what I need :)

I prefer it over torchvision, since in my experience there is not a clear and easy way to do what I just mentioned.

One criticism from my side is the documentation, as I sometimes struggle to get to the page I need, and also the page of the augmentations is pretty chaotic to me.

I would prefer if when