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CNN with FHE to identify if there is a human present in a picture. It was developed using Concrete ML from Zama.
Home Page: https://fhexamples.com
Jupyter Notebook 80.63%
Makefile 0.31%
Python 17.65%
Shell 1.41%
human-in-picture-concrete-ml's Introduction
Human In Picture (HIP) using Concrete ML
- Create a CNN using Concrete ML to identify if there is a human in a picture.
- Identify the performance limits of a model for this problem using Concrete ML.
- Create benchmarks for different sizes of the input.
- You will need to have a Kaggle account in order to download the dataset, we recommend using Google Login.
- After creating an account you will need to download a
kaggle.json file as the API key.
- You can find that file by going to Your Profile and scrolling down to
the
API section.
- Then create a new token and you will download the
kaggle.json.
- Create a new hidden dictory called
.kaggle in the home directory.
- Move
kaggle.json to .kaggle
- Required Python version: 3.10 < 3.11
- This project uses Poetry. If you don't already have Poetry installed,
make deps will install it for you.
- Make sure to have installed
curl before running the command.
- To install dependencies you will need to run:
- To download the dataset you will need to run:
- To run the project only once you'll need to run:
- To run the benchmarks you'll need to run:
- This will run the benchmarks for the following input sizes:
- 32x32
- 64x64
- 96x96
- 128x128
- Brace yourself, this will take a while.
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