Vision Transformers (ViT) are a revolutionary approach to image processing. After achieving great success in natural language processing (NLP), the Transformer architecture has been adapted for image classification and other visual tasks. ViT offers a powerful model as an alternative to traditional convolutional neural networks (CNN) in this domain. It is known for delivering impressive results, especially on large datasets.
In this article, we will discuss the working principle of Vision Transformers, their advantages over CNNs and the areas in which they are used.
Vision Transformers basically divides images into small patches and gives each patch as input to a Transformer model. By learning the context of each part of the images, this method enables successful results in more complex visual tasks.
The working principle of ViT is as follows:
ViT's success is especially noticeable in large datasets. Here are the advantages and challenges of Vision Transformers versus CNNs:
CNNs are strong at learning local features but may struggle to understand global context. ViT learns how each part of the whole image affects each other and provides a broader understanding of context.
Vision Transformers work more efficiently on large datasets. Therefore, ViT can outperform CNNs when trained with millions of images. However, CNNs generally perform better when trained with small datasets.
ViT is computationally more expensive than CNNs. Especially with large datasets, the training time can be long. However, thanks to modern hardware and GPUs, this challenge is being overcome.
ViT has many applications in image processing and computer vision. Here are some of the main use cases:
ViT gives successful results in image classification tasks on large datasets. Especially in the medical field, ViT is widely used in image classification models for disease detection.
In object detection and segmentation tasks, ViT excels at understanding the relationship of each object to other objects. For example, in environmental sensing systems for autonomous vehicles, ViT can more effectively distinguish objects in an image.
ViT can also be used in art and creative applications. For example, in tasks such as Neural Style Transfer, which transforms an image into an artistic style, ViT can help produce a variety of visual effects.
ViT has ushered in a new era in computer vision. It is expected to be further developed and optimized, especially when working with large datasets. In addition, lighter and faster Vision Transformer models can also provide effective results with low datasets. In the coming years, ViT and its derivatives are expected to become more widespread in various industries.
Vision Transformers (ViT) goes beyond traditional CNNs, starting a new era in image processing. ViT is more effective on large datasets and delivers powerful results in contextual information learning.
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