Monday, March 13, 2023

New Tutorial: Train a MaskFormer Segmentation Model with Hugging Face 🤗 Transformers

Hi,

This week you'll learn about Train a MaskFormer Segmentation Model with Hugging Face 🤗 Transformers.

The human nervous system is truly amazing. We take for granted the gift we have in the form of our perception tools. When we look at something, our mind subconsciously classifies everything into its respective labels and distinguishes between them. But teaching a computer to do the same is a task that the leading AI researchers of today are still trying to perfect.

It is said that one of the end goals of AI is to recreate the human brain. While it sounds extremely far-fetched, through the years, we have managed to make significant strides. For example, in the early 2010s, a simple digit recognizer would be a huge deal, while today, we are trying to perfect self-driving cars.

It goes without saying that image segmentation is a very important task in the computer vision world. The best example of its use would be to help self-driving cars properly navigate the real world. But unfortunately, segmentation at the level of a human brain is difficult to achieve. Still, the folks at Facebook Research have developed a state-of-the-art image segmentation model called MaskFormer, which is the spotlight for today's blog post.

The big picture: Facebook Research has developed MaskFormer, a state-of-the-art segmentation model that unifies semantic segmentation, instance segmentation, and panoptic segmentation tasks into a single framework. Today, we use Hugging Face's implementation to train a MaskFormer segmentation model and assess the results.

How it works: Hugging Face provides us with a host of up-to-date, state-of-the-art models that we can easily plug into our project for training and inference purposes. Hugging Face's ease of use also extends to datasets, so training complex state-of-the-art models are condensed into a few lines of code.

Our thoughts: For recreating the latest research, sometimes it can be really hectic and time-consuming to write everything from scratch so that we can ablate the results. Hugging Face's implementations make our lives easier by providing many functionalities, calling these model abstractions, and testing them to our heart's content.

Yes, but: Don't let these ease-of-use abstractions make you lazy and prevent you from figuring out what's happening inside these black bodies. Reviewing the research paper before tackling a complex topic is always wise.

Stay smart: Be sure you don't treat MaskFormer as black bodies. Instead, keep testing them until there is no doubt about how it works!

Click here to read the full tutorial

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