chore: reorg cifar usecases

.github/workflows/refresh-one-notebook.yaml

@@ -50,10 +50,10 @@ env:
   ACTION_RUN_URL: ${{ github.server_url }}/${{ github.repository }}/actions/runs/${{ github.run_id }}
   # --- refresh_notebooks_list.py: refresh list of notebook paths currently available [START] ---
   # --- do not edit, auto generated part by `make refresh_notebooks_list` ---
-  Cifar10: "use_case_examples/cifar_brevitas_with_model_splitting/Cifar10.ipynb" 
-  CifarInFhe: "use_case_examples/cifar_brevitas_finetuning/CifarInFhe.ipynb" 
-  CifarInFheWithSmallerAccumulators: "use_case_examples/cifar_brevitas_finetuning/CifarInFheWithSmallerAccumulators.ipynb" 
-  CifarQuantizationAwareTraining: "use_case_examples/cifar_brevitas_finetuning/CifarQuantizationAwareTraining.ipynb" 
+  Cifar10: "use_case_examples/cifar/cifar_brevitas_with_model_splitting/Cifar10.ipynb" 
+  CifarInFhe: "use_case_examples/cifar/cifar_brevitas_finetuning/CifarInFhe.ipynb" 
+  CifarInFheWithSmallerAccumulators: "use_case_examples/cifar/cifar_brevitas_finetuning/CifarInFheWithSmallerAccumulators.ipynb" 
+  CifarQuantizationAwareTraining: "use_case_examples/cifar/cifar_brevitas_finetuning/CifarQuantizationAwareTraining.ipynb" 
   ClassifierComparison: "docs/advanced_examples/ClassifierComparison.ipynb" 
   ClientServer: "docs/advanced_examples/ClientServer.ipynb" 
   ConvolutionalNeuralNetwork: "docs/advanced_examples/ConvolutionalNeuralNetwork.ipynb" 
@@ -62,7 +62,7 @@ env:
   DecisionTreeRegressor: "docs/advanced_examples/DecisionTreeRegressor.ipynb" 
   Deployment: "docs/advanced_examples/Deployment.ipynb" 
   ExperimentPrivacyTreePaper: "docs/advanced_examples/ExperimentPrivacyTreePaper.ipynb" 
-  FromImageNetToCifar: "use_case_examples/cifar_brevitas_finetuning/FromImageNetToCifar.ipynb" 
+  FromImageNetToCifar: "use_case_examples/cifar/cifar_brevitas_finetuning/FromImageNetToCifar.ipynb" 
   FullyConnectedNeuralNetwork: "docs/advanced_examples/FullyConnectedNeuralNetwork.ipynb" 
   FullyConnectedNeuralNetworkOnMNIST: "docs/advanced_examples/FullyConnectedNeuralNetworkOnMNIST.ipynb" 
   GLMComparison: "docs/advanced_examples/GLMComparison.ipynb" 
@@ -72,7 +72,7 @@ env:
   LinearSVR: "docs/advanced_examples/LinearSVR.ipynb" 
   LogisticRegression: "docs/advanced_examples/LogisticRegression.ipynb" 
   MnistInFHE: "use_case_examples/mnist/MnistInFHE.ipynb" 
-  PerrorImpactOnFMNIST: "use_case_examples/cifar_brevitas_finetuning/PerrorImpactOnFMNIST.ipynb" 
+  PerrorImpactOnFMNIST: "use_case_examples/cifar/cifar_brevitas_finetuning/PerrorImpactOnFMNIST.ipynb" 
   PoissonRegression: "docs/advanced_examples/PoissonRegression.ipynb" 
   QGPT2Evaluate: "use_case_examples/llm/QGPT2Evaluate.ipynb" 
   QuantFrameworkExample: "use_case_examples/llm/QuantFrameworkExample.ipynb" 

use_case_examples/cifar/README.md

@@ -0,0 +1,59 @@
+# 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.Notably, a fine-tuning from a public pre-trained model, a training from scratch using quantization aware training (QAT) and finally a hybrid approach where only a subset of the model is done in FHE.
+
+## Table of Contents
+
+1. [Use-Cases](#use-cases)
+   - [Fine-Tuning VGG11 CIFAR-10/100](#fine-tuning-cifar-10100)
+   - [Training Ternary VGG9 on CIFAR10](#training-ternary-vgg-on-cifar10)
+   - [CIFAR-10 VGG9 with one client-side layer](#cifar-10-with-a-split-clearfhe-model)
+1. [Installation](#installation)
+1. [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 Quantization Aware Training (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).
+1. [Quantizing the pre-trained network](cifar_brevitas_finetuning/CifarQuantizationAwareTraining.ipynb).
+1. [Computing the accuracy of the quantized models with FHE simulation](cifar_brevitas_finetuning/CifarInFhe.ipynb).
+1. [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. It 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**: This method divides the model into two segments: one that operates in plaintext (clear) and the other in Fully Homomorphic Encryption (FHE). This division allows for greater precision in the input layer while taking advantage of FHE's privacy-preserving capabilities in the subsequent layers.
+- **Model Design**: Aims at using 8-bit accumulators to speed up FHE inference. The design incorporates pruning techniques and employs 2-bit weights to meet this aim.
+- **Implementation**: Provides step-by-step guidance on how to execute the hybrid clear/FHE model, focusing on the details and decisions behind selecting the optimal `p_error` value. Special attention is given to the binary search method to balance accuracy and FHE performance.
+
+[Results & Metrics](cifar_brevitas_with_model_splitting/README.md#results)
+
+## Installation
+
+All use-cases can be quickly set up with:
+
+<!--pytest-codeblocks:skip-->
+
+```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)