Facial-Attribute-Recognition

Project Overview

This repository is dedicated to facial attribute recognition using multi-head neural network architectures with PyTorch implemantation.

Installation

Prerequisites

• Python 3.8+
• PyTorch 1.9+
• torchvision
• tqdm
• matplotlib
• tensorboardx
• CUDA

Setup

conda create -y -n att python=3.8
conda activate att
pip install torch==1.9.0+cu111 torchvision==0.10.0+cu111 torchaudio==0.9.0 -f https:/download.pytorch.org/whl/torch_stable.html

Install the required Python packages:

pip install -r requirements.txt

Dataset

You can download the aligned version of Celeba dataset from here.

The train, validation, and test text splits of CelebA, along with the corresponding attribute labels, are available here.

Usage

Training a Model

First, download the pre-trained ResNet-50 model trained on VGGFace from here.

Then, use the 'main.py' script to train the multi-head network. Here's an example of how to run the training process:

python main.py --data ./dataset/Celeba \
    --epochs 10 --batch_size 1500 --learning_rate 0.05 \
    --workers 8
    --test-batch 1500

Contributing

Contributions to this project are welcome. Please fork the repository, make your changes, and submit a pull request.

License

This project is licensed under the MIT License - see the LICENSE file for details. "# Face-Morphing-Attack-Detection-Benchmark" "# Face-Morphing-Attack-Detection-Benchmark"