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Learn how to perform image classification using PyTorch on the CIFAR100 dataset. Follow along with the series from and explore the dataset, view the image elements, and more.

Image Classification with PyTorch

"Traditionally, the only way to get a computer to do something -- from adding two numbers to flying an airplane --was to write down an algorithm explaining how, in painstaking detail. But machine learning, also known as learners, are different: they figure it out on their own, by making inferences from data. And the more data they have, the better the get. Now we don't have to program computers: they program themselves." (from "The Master Algorithm by Pedro Domingo)

Image Classification is a supervised learning problem: define a set of target classes (objects to identify in images), and train a model to recognize them using labeled example photos.

This notebook presents the techniques using PyTorch from the series applied to the CIFAR100 dataset.

I learned a lot. I also learned that I just scratched the surface; there's still deep learning and countless hours of training I need before I can comfortably say - yes "I GOT IT!"

!pip install jovian --upgrade --quiet
#import os
import torch
import torchvision
import numpy as np
#import tarfile
import torch.nn as nn
import torch.nn.functional as F
#from torchvision.datasets.utils import download_url
from torchvision.datasets import CIFAR100
#from torchvision.datasets import ImageFolder
from torchvision.transforms import ToTensor
from torchvision.utils import make_grid
from import random_split
from import DataLoader

import matplotlib
import matplotlib.pyplot as plt
%matplotlib inline

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

CIFAR100 Dataset