Classifying CIFAR100 images using ResNets, Regularization and Data Augmentation in PyTorch

Here, we'll use the following techniques to train a model in less than 5 minutes to achieve over 70% accuracy in classifying images from the CIFAR100 dataset:

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.

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'

project_name='04-cifar100-course-project'