# CIFAR10 App

We deployed privacy preserving CIFAR recognition as the first APP example. 

\
[CIFAR-10](https://www.cs.toronto.edu/~kriz/cifar.html) is a widely recognized dataset comprising 60,000 color images of size 32x32 pixels, categorized into 10 classes such as automobiles, airplanes, dogs, etc. This dataset serves as a standard benchmark for machine learning algorithms in computer vision.

The CIFAR10 APP implements a machine learning model capable of efficiently classifying encrypted images without decrypting them.

#### Input

Each image is encoded as a real vector with a dimension of 3072=3x1024. The initial 1024 slots denote the red channel, the subsequent ones denote green, and the final segment denotes blue. Each slot stores value in the range \[0, 255]. 

#### Output

The outcome of the computation is governed by the initial 10 slots in the resultant ciphertext. If the input image belongs to class `i`, then within the first 10 slots of the resultant vector the maximum value will be located in slot `i`.

\ <br>


---

# 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://docs.fairmath.xyz/fhe-computer/cifar10-app.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.