In the realm of artificial intelligence, face recognition technology has made significant strides, leveraging powerful libraries like PyTorch. This blog post will walk you through the process of setting up a face recognition model using PyTorch, covering installation, preparation, training/evaluation, and more.

Introduction

This repository is dedicated to various experimental face recognition models, including CosFace, ArcFace, SphereFace, and SV-Softmax. Let’s dive into how you can get started.

Contents

• Installation
• Preparation
• TrainEval
• Models
• Face Recognition Demo

Installation

First, you need to set up your environment. Follow these steps to install the necessary packages:

bash
bash init_venv.sh
source venv/bin/activate

Preparation

Next, prepare the data that your model will train on:

1. For face recognition training, you should download the VGGFace2 dataset. We will refer to this folder as $VGGFace2_ROOT.
2. For evaluation, download the LFW dataset and the LFW landmarks. Place everything in a folder referred to as $LFW_ROOT.

TrainEval

Now you’re all set to initiate training or evaluation. Follow these commands:

1. Navigate to your FR_ROOT folder:

bash
cd $FR_ROOT

2. Start training the face recognition model:

bash
python train.py --train_data_root $VGGFace2_ROOT/train --train_list $VGGFace2_ROOT/meta/train_list.txt --train_landmarks  $VGGFace2_ROOT/bb_landmark --val_data_root $LFW_ROOT/lfw --val_list $LFW_ROOT/pairs.txt --val_landmarks $LFW_ROOT/lfw_landmark.txt --train_batch_size 200 --snap_prefix mobilenet_256 --lr 0.35 --embed_size 256 --model mobilenet --device 1

3. To evaluate the trained model (assuming we have trained a MobileNet model with 256 embedding size for 300k iterations):

bash
python evaluate_lfw.py --val_data_root $LFW_ROOT/lfw --val_list $LFW_ROOT/pairs.txt --val_landmarks $LFW_ROOT/lfw_landmark.txt --snap path/to/snapshot/mobilenet_256_300000.pt --model mobilenet --embed_size 256

Configuration Files

Instead of providing all parameters through the command line, consider using a YAML configuration file. Each line should contain a valid parameter description. For instance:

# optimizer parameters
lr: 0.4
train_batch_size: 256

# loss options
margin_type: cos
s: 30
m: 0.35

# model parameters
model: mobilenet
embed_size: 256

# misc
snap_prefix: MobileFaceNet
devices: [0, 1]

# datasets
train_dataset: vgg
train_data_root: $VGGFace2_ROOT/train

To use this config file, pass its path when running the training script:

bash
python train.py -m 0.35 @.my_config.yml # 'm' can be overwritten using my_config.yml

Models

You can download the pretrained model from the following link:

• Mobilenet_se_focal_121000.pt

Navigate to your FR_ROOT to evaluate:

bash
cd $FR_ROOT
python evaluate_lfw.py --val_data_root $LFW_ROOT/lfw --val_list $LFW_ROOT/pairs.txt --val_landmarks $LFW_ROOT/lfw_landmark.txt --snap path/to/snapshot/Mobilenet_se_focal_121000.pt --model mobilenet --embed_size 256

Upon successful evaluation, you should see an output akin to:

AccuracyVal_same_accuracy mean: 0.9923
AccuracyVal_diff_accuracy mean: 0.9970
AccuracyVal_accuracy mean: 0.9947
AccuracyVal_accuracy std dev: 0.0035
AUC: 0.9995
Estimated threshold: 0.7241

Face Recognition Demo

For setting up a demo, refer to the details in the Face Recognition demo with OpenVINO Toolkit.

Troubleshooting

If you run into issues while setting up your face recognition model, consider the following troubleshooting ideas:

• Ensure that your environment is correctly activated and all dependencies are installed.
• Double-check the paths for your dataset to ensure they are correct.
• Review your configuration file for any syntax errors or incorrect parameter values.
• Confirm that your GPU is correctly configured and has sufficient memory for training.

For additional insights, updates, or to collaborate on AI development projects, stay connected with fxis.ai.

At fxis.ai, we believe that such advancements are crucial for the future of AI, as they enable more comprehensive and effective solutions. Our team is continually exploring new methodologies to push the envelope in artificial intelligence, ensuring that our clients benefit from the latest technological innovations.