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# CIFAR10 App

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

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[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`.

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