groupImg VS albumentations

If anyone is interested, I have two projects that uses k-means

https://github.com/victorqribeiro/groupImg

https://github.com/victorqribeiro/budget

Being one of the first ML algorithms that I learned, I spend some time finding use cases for it

If I'm not mistaken I've also used in to classify deforestation in an exercise

well, I took a class back in college with an Old School professor. He's the one who drove the class that way. I just enjoyed the process. But there are a lot of tutorials on the internet about writing your own NN. I think the first algorithm that I ever wrote regarding ML was k-means [1]. Start there and see where it takes you:

https://en.wikipedia.org/wiki/K-means_clustering

Look at this project I have used it:

https://github.com/victorqribeiro/groupImg

You can use albumentations if you are comfortable with using open source libraries https://github.com/albumentations-team/albumentations

One of the members of the core team of our open-source library https://albumentations.ai/

It was not the only reason he was hired; it was a solid addition to his already good performance at the interviews.

Or at least that is what the hiring manager later said.

I am one of the creators and maintainers of https://albumentations.ai/.

- 12800+ stars

Is something planned to support data augmentations? Something like https://albumentations.ai/

Here you go: https://albumentations.ai/

I think they should use some data augmentation techniques like I am using for Infinity AI if you wanna see more here. Note that most of these do not work for image generation.

Our second approach was a more automated one. Here the idea was to try out an image augmentation library, Albumentations, and use it for adversarial attacks. This time, instead of one-shot images, we applied the transformations at random time ranges. We chose for these transformations also to be more subtle than then one-shot images, such as vertical flips, grayscaling, downscaling, …

Check out albumentations. If your use case is segmentation, check out the offline augmentation of this project

I think this is the most popular open source project: https://github.com/albumentations-team/albumentations

You can also curate your own dataset by using open source images (https://universe.roboflow.com/search?q=faces%20images%3E1000) and open source augmentations (https://github.com/albumentations-team/albumentations). Or you can do use the augmentation UI (https://docs.roboflow.com/image-transformations/image-augmentation) to apply noise, blurring, shear, crop, etc.