Face-Recognition-pipeline-using-SOTA-ArcFace

Face Recognition pipeline using SOTA ArcFace using Pytorch is inspired from the paper ArcFace: Additive Angular Margin Loss for Deep Face Recognition

Problem Statement

The problem statement for face recognition task can be found SkylarkLabs-ai-link

The folders contain around 50 cropped face images of each avenger can be found from kaggle Avengers face recognition!

The dataset zip file or folder can be found from drive link

The detailed code implementation for the ArcFace implementation using pytorch can be found from colab notebook

We have choosen MobileFacenet model, which is a lightweight model as compared to the vailable pre-trained IR-SR50 model,
We have done transfer learning using pre-trained mobile-facenet model trained on MS1M, VGG2, Emore dataset.
The trained best model evaluated on validation data(lowest validation loss) can be foudn here
More on the models, implementation can be found InsightFace_Pytorch - Arcface Pytorch Implementation github

The train.py, test.py and related utilities to execute train, test pipeline can be found from utilities folder

    git clone [repo_name]

    cd Face-Recognition-pipeline-using-SOTA-ArcFace
    pip install -r requirements.txt

Navigate to "utilities folder" which contains dependencies to run the pipeline - utilities_path

    cd [utilities_path]

Execute train pipeline - pre-trained-mobilefacenet model folder. The train_pipeline code can be tweaked little to change the pre-trained model file.

    python train.py -dataset [dataset_path] -model [pre-trained-mobilefacenet model folder]

trained_mode_folder. The train_pipeline code can be tweaked little to change the pre-trained model file.
csv_folder_demo, the path where the train, valid, test csv files are stored from the dataframe

    python test.py -dataset [dataset_path] -model [trained_mode_folder] -df [csv_folder]

A simple demo of that train, test pipeline can be found here.
Docker implementation in colab can be found here, though it needs more testing to execute the layers.