Learn how to perform image classification using PyTorch on the CIFAR100 dataset. Follow along with the series from jovian.ai and explore the dataset, view the image elements, and more.
"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 https://jovian.ai/learn/deep-learning-with-pytorch-zero-to-gans 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 torch.utils.data import random_split from torch.utils.data import DataLoader import matplotlib import matplotlib.pyplot as plt %matplotlib inline matplotlib.rcParams['figure.facecolor'] = '#ffffff'