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Updated 3 years ago
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'