Are Convolutional Neural Networks (CNNs) still the most suitable solution for agricultural computer vision tasks such as plant species classification or disease detection? We could debate it since the technology revolutionizing natural language processing also makes waves in computer vision. Let’s talk about Transformers, which have achieved remarkable success in various computer vision tasks, competing with and sometimes outperforming the previously dominant CNN architectures.
The Transformer architecture, initially proposed for tackling complex language problems, has demonstrated incredible performance in understanding context, dealing with long-range dependencies, and generating coherent language. However, recently, the adaptability of these architectures has been explored beyond the boundaries of language, moving into the realm of images.
Transformers look at an image as a sequence of pixels, similar to how we would read a text as a sequence of words. This is precisely where the concept of ‘image patching’ comes into play. Image patching is a method to break down an image into smaller chunks or ‘patches.’ Each patch is then seen as an ‘individual word’ within the ‘sentence’ that is the entire image. Therefore, Transformers enable us to model the complex interdependencies between different parts of an image. We can then use these models for a wide range of tasks, from image classification to object detection, semantic segmentation, and beyond.
But we’re just scratching the surface! There’s still so much potential to be unlocked in this field; the next breakthrough could be just around the corner.
Whether you’re a data scientist, AI enthusiast, or just love staying updated on cutting-edge technologies, we recommend checking our notebooks related to the Transformers.
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