What does "Transformer Modules" mean?
Table of Contents
Transformer modules are parts of a model used mainly in the field of machine learning. They are good at processing information, especially when working with long sequences of data, like images taken from space that have many colors and details.
How They Work
These modules break down information into smaller pieces, allowing them to look at each piece separately and understand it better. This means that when analyzing images, the model can pay attention to local details, like textures, and wider patterns, like overall shapes.
Benefits
Using transformer modules helps in making sense of complex data more efficiently. They can handle different levels of detail, which allows for better analysis of images. This makes them useful in many tasks, such as classifying images or segmenting parts of images.
Applications
One popular use of transformer modules is in image classification, where they help identify what is shown in a picture. They are also useful for segmenting images, which means breaking down an image into its parts to analyze it further.