Introduction to Vision Transformer
The Vision Transformer, also known as ViT, is a model used for image classification that utilizes a Transformer-like architecture over patches of an image. This approach splits the image into fixed-size patches, and each patch is linearly embedded, added with position embeddings, and then fed into a standard Transformer encoder. To perform classification, an extra learnable "classification token" is added to the sequence.
What is a Transformer?
A Transformer is a neural network architecture that uses self-attention to process a sequence of input data. The self-attention mechanism allows for contextual relationships between all of the input elements, which can be used to perform operations such as classification, translation, and summarization.
How does ViT work?
ViT works by splitting an input image into patches, which are then processed individually by a linear embedding layer. This embedding layer maps each patch to a vector representation, which is then concatenated with a position embedding vector that encodes the position of the patch within the image. The resulting sequence of vectors is then passed through a standard Transformer encoder.
By processing an image in this way, ViT is able to capture global relationships between patches, allowing it to effectively classify images without relying on traditional convolutional neural network (CNN) architectures, which have been the standard for image classification tasks.
Benefits of ViT
One of the primary benefits of ViT is its ability to process images using only a linear embedding layer, which allows for greater flexibility in the size and dimensions of input images. This makes ViT well-suited to tasks such as image classification, object detection, and segmentation, where the size and shape of input images can vary widely.
In addition to its flexibility, ViT has also been shown to outperform traditional CNN architectures on a number of image classification tasks. This is in part due to the ability of the Transformer architecture to capture long-range dependencies between input elements, which can be difficult for CNNs to model effectively.
Limitations of ViT
While ViT has shown great promise for image classification tasks, it does have some limitations. One of the primary limitations of ViT is its computational complexity, which can make training and inference times prohibitively long for large-scale applications. In addition, ViT requires significantly more training data than traditional CNN architectures in order to achieve good performance.
The Vision Transformer is a powerful approach to image classification that utilizes a Transformer-like architecture over patches of an image. By processing images in this way, ViT is able to capture global relationships between patches, allowing it to effectively classify images without relying on traditional CNN architectures. While ViT has some limitations, its flexibility and superior performance on image classification tasks make it an important area of research in modern computer vision.
