Master Thesis - Detection of persons and evaluation of gender and age in image data
xdobis01 186654 2019/2020 UBMI FEKT
supervisor doc. Ing. Radim Kolar Phd.

First, download folders with weighs for each network
and from project_data download AVS and UTK data sets 

please respect folder structure if you
dont want to alter manually paths in the scripts. 

Link: 
https://vutbr-my.sharepoint.com/:f:/g/personal/kolarr_vutbr_cz/EiAJU2k5BxtEjJN0aHLVs0cBk5FHcXG3v8EQvXaoWNvYiA?e=5%3agMUsHX&at=9





ret - RetinaFace folder with
	model - folder with network model
	weights - contains ResNet and MobileNet RetinaFace variants of weights

cor - CORAL folder with 
	model - folder with network model and dataset classes 
	weights - folder with network weights
		pretrained - weights from original CORAL authors
		new - trained in this thesis
			UTK   - best trained UTK weights in thesis refered as 1-100 UTK
			LSTM  - CORAL-LSTM weights trained in this thesis
				Layer_1 - CORAL-LSTM with 1 LSTM layer
				Layer_2 - CORAL-LSTM with 2 LSTM layer
					Unfreezed_weights - folder with weights from unfreeze tests
				Layer_3 - CORAL-LSTM with 3 LSTM layer

gem - gender and emotion network folder
	weights - weights for both networks (Keras loads model from weights)

DATA - 

utils - utility code

TestDir - default data directory for scripts if user does not give data directory as input

RESULTS - default save directory for scripts if user does not give save directory as input


runs - folder with tensorboard runs


RetinaFace Wider Face evaluation was done just with code from RetinaFace authors and 
is not part of this thesis project. For more information visit RetinaFace link below
or implementation in semestral part of this thesis.

Authors of reused CNN architectures and utility code.

RetinaFace

Deng, J., Guo, J., Zhou, Y., Yu, J., Kotsia, I. and Zafeiriou, S., 2019. 
RetinaFace: Single-stage Dense Face Localisation in the Wild.
arXiv preprint arXiv:1905.00641.

https://github.com/biubug6/Pytorch_Retinaface

CORAL

Cao, W., Mirjalili, V. and Raschka, S., 2019. 
Consistent rank logits for ordinal regression with convolutional neural networks.
arXiv preprint arXiv:1901.07884.

https://github.com/Raschka-research-group/coral-cnn

Gender and emotion

Arriaga, O., Valdenegro-Toro, M. and Plöger, P., 2017. 
Real-time convolutional neural networks for emotion and gender classification. 
arXiv preprint arXiv:1710.07557. 

https://github.com/oarriaga/face_classification