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use_case_examples/cifar/README.md

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+# CIFAR-10 and CIFAR-100 Classification with FHE
+
+This repository provides resources and documentation on different use-cases for classifying CIFAR-10 and CIFAR-100 images using Fully Homomorphic Encryption (FHE). Each use-case demonstrates different techniques and adaptations to work within the constraints of FHE.
+
+## Table of Contents
+1. [Use-Cases](#use-cases)
+   - [Fine-Tuning CIFAR-10/100](#fine-tuning-cifar-10100)
+   - [Training Ternary VGG on CIFAR10](#training-ternary-vgg-on-cifar10)
+   - [CIFAR-10 with a Split Clear/FHE Model](#cifar-10-with-a-split-clearfhe-model)
+2. [Installation](#installation)
+3. [Further Reading & Resources](#further-reading--resources)
+
+## Use cases
+
+### Fine-Tuning CIFAR-10/100
+
+- **Description**: This use-case explores how to convert a pre-trained CNN (on imagenet) to its FHE equivalent using QAT and Concrete ML. The conversion process involves adapting a VGG11 network and quantizing the network for FHE.
+
+- Notebooks:
+1. [Adapting VGG11 for CIFAR datasets](cifar_brevitas_finetuning/FromImageNetToCifar.ipynb).
+2. [Quantizing the pre-trained network](cifar_brevitas_finetuning/CifarQuantizationAwareTraining.ipynb).
+3. [Computing the accuracy of the quantized models with FHE simulation](cifar_brevitas_finetuning/CifarInFhe.ipynb).
+4. [Enhancing inference time in FHE using smaller accumulators](cifar_brevitas_finetuning/CifarInFheWithSmallerAccumulators.ipynb).
+
+[Results & Metrics](cifar_brevitas_finetuning/README.md#results)
+
+### Training Ternary VGG on CIFAR10
+
+- **Description**: Train a VGG-like neural network from scratch using Brevitas on CIFAR-10 and run it in FHE. This use-case modifies the original VGG model for compatibility with Concrete ML, and explores the performance gains of rounding operations in FHE.
+- **Training & Inference**: Scripts provided to train the network and evaluate its performance. Also includes simulations in Concrete ML and insights into the performance enhancement using rounding.
+
+[Results & Metrics](cifar_brevitas_training/README.md#Accuracy_/and_/performance)
+
+### CIFAR-10 with a Split Clear/FHE Model
+
+- **Description**: An approach that splits the model into two parts: one running in clear and the other in FHE. By doing this, higher precision can be achieved in the input layer while still benefiting from FHE in subsequent layers.
+- **Model Design**: Targets 8-bit accumulators for faster FHE inference. Pruning and 2-bit weights are used.
+- **Implementation**: Detailed steps on how to run the model in FHE and the trade-offs involved in choosing the appropriate p_error value.
+
+[Results & Metrics](cifar_brevitas_with_model_splitting/README.md#results)
+
+## Installation
+
+All use-cases can be quickly set up with:
+
+```bash
+pip install -r requirements.txt
+```
+
+## Further Reading & Resources
+
+- [Concrete ML Documentation](https://docs.zama.ai/concrete-ml/)
+- [Brevitas Github Repository](https://github.com/Xilinx/brevitas)
+- [Fully Homomorphic Encryption Basics](https://www.zama.ai/post/tfhe-deep-dive-part-1)
+- [CIFAR Datasets Overview](https://www.cs.toronto.edu/~kriz/cifar.html)