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Classifying GENDER images using ResNets, Regularization and Data Augmentation in PyTorch

OBJECTIVE

It is a dataset containing images of male and female faces,and by creating and training a model using PyTorch we are going to classify an facial image by gender.

we'll use the following techniques to train a state-of-the-art model

  • Data normalization
  • Data augmentation
  • Residual connections
  • Batch normalization
  • Learning rate scheduling
  • Weight Decay
  • Gradient clipping
  • Adam optimizer

Let's begin by installing and importing the required libraries.

# Uncomment and run the appropriate command for your operating system, if required
# No installation is reqiured on Google Colab / Kaggle notebooks

# Linux / Binder / Windows (No GPU)
# !pip install numpy matplotlib torch==1.7.0+cpu torchvision==0.8.1+cpu torchaudio==0.7.0 -f https://download.pytorch.org/whl/torch_stable.html

# Linux / Windows (GPU)
# pip install torch==1.7.1+cu110 torchvision==0.8.2+cu110 torchaudio==0.7.2 -f https://download.pytorch.org/whl/torch_stable.html
 
# MacOS (NO GPU)
# !pip install numpy matplotlib torch torchvision torchaudio
import os
import torch
import torchvision
import tarfile
import torch.nn as nn
import numpy as np
import torch.nn.functional as F
from torchvision.datasets.utils import download_url
from torchvision.datasets import ImageFolder
from torch.utils.data import DataLoader
import torchvision.transforms as tt
from torch.utils.data import random_split
from torchvision.utils import make_grid
import matplotlib
import matplotlib.pyplot as plt
%matplotlib inline

matplotlib.rcParams['figure.facecolor'] = '#ffffff'