A vision transformer (ViT) is a type of neural network that can be used for image classification and other computer vision tasks. It marks an interesting evolution of various methods that deal with sequential data. We’ve gone from understanding the drawbacks of recurrent neural networks (RNNs) to focusing on long short-term memory (LSTM) to vision transformers, which is used for image and vision applications.
Vision Transformer Definition
A vision transformer is a type of neural network that’s used for image classification and other computer vision tasks. It does so by representing images as a sequence of patches that represent vectors, which are then fed into a transformer encoder to teach the model.
Before diving into vision transformers, I had a few questions that you might also have, including:
- Can I use embeddings and positional encodings for images?
- Can I use a prompt-based approach for the image?
- Can I use transformer architecture for an image?
The short answer is yes, vision transformers can do each of those things. Let’s look at the basics of vision transformers.
What Is a Vision Transformer?
A vision transformer (ViT) is a type of neural network that can be used for image classification and other computer vision tasks. ViTs are based on the transformer architecture, which was originally developed for natural language processing (NLP) tasks. However, ViTs make some key changes to the transformer architecture to make it better suited for image processing.
One of the key changes that ViTs make is the way that they represent images. In NLP, transformer models typically represent text as a sequence of words. However, images can’t be represented as a sequence of words. Instead, ViTs represent images as a sequence of patches.
How Does a Vision Transformer Work?
Vision transformers first divide the image into a sequence of patches. Each patch is then represented as a vector. The vectors for each patch are then fed into a transformer encoder. The transformer encoder is a stack of self-attention layers. Self-attention is a mechanism that allows the model to learn long-range dependencies between the patches. This is important for image classification, as it allows the model to learn how the different parts of an image contribute to its overall label.
The output of the transformer encoder is a sequence of vectors. These vectors represent the features of the image. The features are then used to classify the image.
Advantages of Vision Transformer
There are a number of benefits to using Vision Transformers for image classification.
ViTs can learn global features of images. This is because they are able to attend to any part of the image, regardless of its location. This can be helpful for tasks such as object detection and scene understanding. ViTs aren’t as sensitive to data augmentation as CNNs. This means that they can be trained on smaller data sets.
ViTs can be used for a variety of image classification tasks. This includes tasks such as object detection, scene understanding and fine-grained classification.
Disadvantages of a Vision Transformer
There are a number of drawbacks to using vision transformers for image classification.
Vision transformers are computationally expensive to train. Any image related task is always expensive due to large pixel sizes of images. This is because they have a large number of parameters. ViTs are not as efficient as convolutional neural networks (CNNs) at processing images. This is because they need to attend to every part of the image, even if it is not important for the task at hand.
ViTs aren’t as interpretable as CNNs. This means that it is difficult to understand how they make predictions.
Vision transformers are a promising new approach to image classification. They have a number of potential benefits, but they also have some drawbacks. As ViTs continue to develop, it will be interesting to see how they compare to CNNs on a variety of image classification tasks.