Visiontransformerit

Visiontransformerit, commonly referred to as Vision Transformer (ViT), is a neural network architecture that adapts the Transformer model from natural language processing to image recognition. It treats a fixed-size image as a sequence of patches, each patch converted to an embedding vector, and processes the sequence with a standard Transformer encoder.

An image is divided into patches (for example 16×16 pixels), each patch is flattened and projected through a trainable linear embedding. Learnable positional embeddings are added, and a special classification token is prepended. The resulting sequence is passed through multiple layers of multi-head self-attention and feed-forward networks, with residual connections and layer normalization.

During training, ViT models are pretrained on large-scale image datasets and then fine-tuned on target tasks. They often benefit from very large data and compute. To improve data efficiency, variants such as Data-efficient Image Transformer (DeiT) introduce distillation tokens and strong data augmentation, enabling good performance on smaller datasets.

ViT can serve as a backbone for detection, segmentation, and other vision tasks, often by combining with task-specific heads or using DETR-inspired adaptations. Compared with convolutional neural networks, ViT exhibits strong performance with large datasets and simpler inductive biases, but historically requires more data and compute, and may be less sample-efficient without pretraining.

Originating from the 2020 work by Dosovitskiy et al. from Google Research, Vision Transformer has inspired numerous variants, optimizations, and hybrid architectures that balance bias and scalability.