What are Vision Transformers (ViT)?

Vision Transformers (ViT): The Transformer Revolution in Image Processing

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.

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Key Features of ViT:

• Patch Representations: An image is divided into small patches of fixed size. Each patch is modeled according to the information contained in a set of pixels.
• Transformer Architecture: As in natural language processing models, the Self-Attention mechanism is used here. This mechanism learns how each patch relates to other patches in the image.
• Efficiency: ViT performs impressively, especially on large datasets. It may be less efficient than CNNs when trained on small datasets, but it excels on large data.

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How Vision Transformers Work?

The working principle of ViT is as follows:

1. Patch Creation: An image is divided into small pieces (patches). For example, a 224x224 pixel image is divided into 16x16 sized patches and each patch contains 16x16 pixels. These patches are used as input representation for ViT.
2. Patch Embedding: Each patch is transformed into a set of vectors through a linear layer. This makes the information of each patch relevant to the model.
3. Positional Encoding: As in natural language processing, the Transformer architecture uses positional encoding to learn sequential information. This encoding is added so that each patch can understand its position in the image.
4. Transformer Blocks: Each patch in the image goes through the Self-Attention mechanism to learn its relationship with other patches. In this way, the model learns both the local and global context of each patch.
5. Classification Layer: In the final stage, the model sends all the learned information to a classification layer and determines to which class the image belongs.

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ViT and CNN Comparison

ViT's success is especially noticeable in large datasets. Here are the advantages and challenges of Vision Transformers versus CNNs:

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1. Contextual Information Learning

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.

2. Data Need

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.

3. Computational Cost

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.

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Usage Areas of Vision Transformers

ViT has many applications in image processing and computer vision. Here are some of the main use cases:

1. Image Classification

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.

2. Object 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.

3. Art and Creative Applications

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.

The Future of Vision Transformers

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.

Conclusion

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.