DeiTSmall

DeiTSmall is a lightweight implementation of the Data-efficient Image Transformer (DeiT) model. DeiT itself is a vision transformer designed to achieve high performance with significantly less training data compared to earlier transformer architectures. DeiTSmall is a smaller version of the original DeiT, meaning it has fewer parameters and a reduced computational footprint. This makes it more suitable for applications where computational resources are limited, such as mobile devices or embedded systems, or for faster experimentation and prototyping.

The core innovation of DeiT lies in its training strategy, which incorporates a data-efficient training methodology. This includes the use of a distillation token, which helps the transformer learn from a teacher model (often a convolutional neural network) more effectively. DeiTSmall benefits from this same training paradigm, allowing it to achieve competitive accuracy on various image classification tasks despite its smaller size. It typically uses a base configuration with fewer layers and smaller hidden dimensions compared to larger DeiT variants. Researchers and developers often use DeiTSmall to quickly test ideas or deploy models in resource-constrained environments without sacrificing too much performance.