# Transformer for Image

Transformer: computationally efficient, scalability, and can train over 100B  parameters.

This paper applies transformer directly to images. On large datasets, BiT attains excellent result.

<figure><img src="/files/igM8Cmi2tQKKZFT2eHJb" alt=""><figcaption><p>Encoding</p></figcaption></figure>

Method:

ViT uses patch embedding, flatten the patches and map to D dimensions with trainable linear projection. MLP.&#x20;

The biggest difference is the encoding process, which is shown here.


---

# Agent Instructions: Querying This Documentation

If you need additional information that is not directly available in this page, you can query the documentation dynamically by asking a question.

Perform an HTTP GET request on the current page URL with the `ask` query parameter:

```
GET https://tianyi0216.gitbook.io/blog/reading-notes/ml-readings/transformer-for-image.md?ask=<question>
```

The question should be specific, self-contained, and written in natural language.
The response will contain a direct answer to the question and relevant excerpts and sources from the documentation.

Use this mechanism when the answer is not explicitly present in the current page, you need clarification or additional context, or you want to retrieve related documentation sections.