Python 3.9.7
Tensorflow 2.8.0 (but the part of saving model and creating pb file needs Tesnsorflow 1.12)
Pandas
NumPy
CV2
matplotlib
PIL
tqdm
flask
flask_restful

./
├── Datasets/
├── Testing/  
├── Training/
├── api.py
├── face_detector.py
└── try_detector.ipynb

./Testing/
├── face_detector.py
├── model.pb
├── predict_for_FDDB.ipynb
├── visulaize_original_annotations.ipynb
├── prepare_data/
    └── explore_and_convert_FDDB.ipynb
|
├── eval_result/
    ├── create_roc_files.py
    ├── plot_roc.ipynb
    └── FDDB-result/
        ├── ContROC.txt
        └── DiscROC.txt
|
└── fddb/
    ├── fddb_images/
    ├── fddb_folds/                 :Contains files with names: FDDB-fold-xx.txt and FDDB-fold-xx-ellipseList.txt, where xx = {01, 02, ..., 10} represents the fold-index.
    |                               Each line in the "FDDB-fold-xx.txt" file specifies a path to an image in the data set. The corresponding annotations are included in the file "FDDB-fold-xx-ellipseList.txt"
    |
    ├── result/
        ├── detections.txt
        ├── ellipseList.txt
        ├── faceList.txt
        ├── ann_images/              :Contains the FDDB images with the detected bounding boxes 
        └── images/
    |
    └── val/
        ├── annotations/             :Contains the FDDB images with the ground truth boxes 
        ├── original_ann_images/     :Contains the FDDB images with the ground truth boxes 
        └── images/

./Training/
├── config.json
├── create_tfrecords.py
├── evaluation_utils.py
├── model.py
├── test_input_pipeline.ipynb
├── train.py
├── train_model.ipynb
├── prepare_data/
    ├── explore_and_prepare_WIDER.ipynb
    └── explore_and_prepare_MAFA.ipynb
|
├── mafa/
    ├── result/
        ├── test/
            └── annotations/          :Contains the annotaions of each test image in JSON files 
        └── train/
            └── annotations/          :Contains the annotaions of each train image in JSON files
    |
    ├── train_shards/                 :Contains the tfrecords of each training shard
    └── val_shards/                   :Contains the tfrecords of each validation shard
|
├── models/
    └── run02/                        :Contains the training checkpoints
|
├── save_&_create_pb
    ├── create_pb.py
    ├── evaluation_utils.py
    ├── face_detector.py
    ├── model.py
    ├── save.py
    ├── export/
        └── run02/                    :Contains the exported saved model
    |
    └── src/
        ├── __init__.py
        ├── anchor_generator.py
        ├── constants.py
        ├── detector.py
        ├── losses_and_ohem.py
        ├── network.py
        ├── training_target_creation.py
        ├── input_pipeline/
            ├── __init__.py
            ├── other_augmentations.py
            ├── pipeline.py
            └── random_image_crop.py
        |
        └── utils/
            ├── __init__.py
            ├── box_utils.py
            └── nms.py
|
└── src/
    ├── __init__.py
    ├── anchor_generator.py
    ├── constants.py
    ├── detector.py
    ├── losses_and_ohem.py
    ├── network.py
    ├── training_target_creation.py
    ├── input_pipeline/
        ├── __init__.py
        ├── other_augmentations.py
        ├── pipeline.py
        └── random_image_crop.py
    |
    └── utils/
        ├── __init__.py
        ├── box_utils.py
        └── nms.py