• Test accuracy comparison between MLP and Deep Learning

• Train Neural Network for normal person and sheikh images classification

• train.ipynb

• inference.py

contain images from two classes, normal person and sheikh

For inference run the following command:

!python3 inference.py --input_image test/image3.jpg

• Train Neural Network for normal person and sheikh images classification

• train.ipynb

• bot.py

contain images from two classes, normal person and sheikh

1. Click here to open the chat with the bot in the Telegram app

2. Start the bot and send him a photo

• train.ipynb

• preprocess.py

• inference.py

Preprocess stage consists of 4 common stages: detect, align, represent and verify. link: Github

1. You must first install retinaface:

!pip install retina-face

1. Run the following command to apply preprocessing:

!python3 preprocess.py --input_images_dir "./input_images" --output_dir "./output_dir"

Contain images from 17 classes of flowers in two subset, train and test.

Dataset link: Flowers

Comparison accuracy of pretrained models that used in transfer learning on test data:

• Train Neural Network on UTKFace dataset using tensorflow and keras

• train.ipynb

• inference.py

Dataset link: UTKFace-dataset

1- First install retina-face

!pip install retina-face

2- Run the following command:

!python3 inference.py --image_path 'input/08.jpg'

• Train Neural Network on gender-recognition dataset using tensorflow and keras

• train.ipynb

• inference.py

Dataset link: celeba-dataset

• Train Neural Network for house price prediction using images

• train.ipynb

• inference.py

Reference: KerasRegressionandCNNs

• Training DCGAN on Mnist dataset

Reference: tensorflow-tutorials

• Face Generator, Training DCGAN on celeba dataset

Dataset link: celeba-dataset