CrossFace

Seamless transform face embeddings across embedder models.

Preview

Installation

pip install -r requirements.txt

Setup

This config.ini utilizes the MegaFace dataset to train the CrossFace model for SimSwap.

[training.dataset]
file_pattern = .datasets/megaface/**/*.jpg

[training.loader]
batch_size = 256
num_workers = 8
split_ratio = 0.95

[training.model]
source_path = .models/arcface_w600k_r50.pt
target_path = .models/arcface_simswap.pt

[training.trainer]
learning_rate = 0.001
max_epochs = 4096
strategy = auto
precision = 16-mixed
logger_path = .logs
logger_name = crossface_simswap

[training.output]
directory_path = .outputs
file_pattern = crossface_simswap_{epoch}_{step}
resume_path = .outputs/last.ckpt

[exporting]
directory_path = .exports
source_path = .outputs/last.ckpt
target_path = .exports/crossface_simswap.onnx
ir_version = 10
opset_version = 15

Training

Train the model.

python train.py

Launch the TensorBoard to monitor the training.

tensorboard --logdir=.logs

Exporting

Export the model to ONNX.

python export.py