prompter: How long of an embedding vector is common for a vision transformer?

assistant: Vision Transformers commonly use embedding vectors of size:

768 (e.g. ViT-Base)
1024 (e.g. ViT-Large)
2048 (e.g. ViT-Huge)

Andrei-Cristian Rad explains in his article on Medium that the ViT architecture uses a trainable embedding tensor of shape (p²*c, d), which learns to linearly project each flat patch to dimension d. This dimension d is constant in the architecture and is used in most of the components.

So anywhere from 768 to 2048 dimensions is common and has been explored in research.
In general, larger embedding sizes allow the model to capture more fine-grained relationships and representations, but also increase parameter count and risk of overfitting. So it's a tradeoff, and the exact size used will depend on the specific use case and data available.

assistant: The length of an embedding vector for a vision transformer varies depending on the specific model architecture and its intended use case. However, commonly used transformer models such as the ViT (Vision Transformer) use embedding vectors that are typically in the range of 768 to 1024 dimensions.

For example, the popular ViT-B/16 model, which was introduced in the original ViT paper, uses a 768-dimensional embedding vector. This vector is generated by feeding an input image through the ViT's transformer network, which is composed of multiple attention-based layers.

There are other variants of the ViT architecture, such as the ViT-Large and ViT-Huge, which use larger models with more parameters and deeper transformer networks. These models typically use larger embedding vectors, such as 1024-dimensional vectors.

In general, larger embedding vectors can provide more expressive power and allow for better representation of complex features in the input image. However, they also require more computational resources and can be more challenging to train. As a result, the choice of embedding vector size depends on the specific requirements of the task and the available computational resources.

assistant: A vision transformer processes images using a series of self-attention layers. Before processing, the image is divided into fixed-size non-overlapping patches (e.g., 16x16 pixels), which are then linearly embedded into a continuous vector representation. These embedded vectors are then processed through the transformer layers.

The length of the embedding vector is typically determined by the design of the vision transformer and the size of the input image patches. Common vision transformers like ViT (Vision Transformer) by Google use an embedding vector size of 768 or 1024 dimensions. Other variations of vision transformers, like DeiT (Data-efficient Image Transformer), also use similar embedding sizes. However, the specific length of the embedding vector may vary depending on the specific architecture and the desired trade-off between model complexity, performance, and efficiency.

In general, the length of the embedding vector in vision transformers is determined by the model design and can range from a few hundred to a few thousand dimensions. The larger the vector size, the more expressive the model's representation, but this comes at the cost of increased computational complexity and memory requirements.