Implementing a Classification Model with Pytorch for CIFAR-10

In this tutorial, we will build a classification model using PyTorch on the CIFAR-10 dataset. CIFAR-10 is a well-known dataset that consists of 60,000 32×32 color images in 10 classes, with 6,000 images per class. The classes are: airplane, automobile, bird, cat, deer, dog, frog, horse, ship, and truck.

To follow along with this tutorial, you will need to have PyTorch installed on your system. You can install PyTorch using pip by running the following command:

pip install torch torchvision

Once you have PyTorch installed, you can proceed with building the classification model on CIFAR-10. We will first load the CIFAR-10 dataset using the torchvision library, and then build and train a simple convolutional neural network (CNN) model for classification.

Step 1: Load and preprocess the CIFAR-10 dataset

First, let’s import the necessary libraries and load the CIFAR-10 dataset using torchvision:

import torch
import torchvision
import torchvision.transforms as transforms

# Load the CIFAR-10 dataset
transform = transforms.Compose([
    transforms.ToTensor(),
    transforms.Normalize((0.5, 0.5, 0.5), (0.5, 0.5, 0.5))
])

trainset = torchvision.datasets.CIFAR10(root='./data', train=True,
                                        download=True, transform=transform)
trainloader = torch.utils.data.DataLoader(trainset, batch_size=4,
                                          shuffle=True, num_workers=2)

testset = torchvision.datasets.CIFAR10(root='./data', train=False,
                                       download=True, transform=transform)
testloader = torch.utils.data.DataLoader(testset, batch_size=4,
                                         shuffle=False, num_workers=2)

classes = ('plane', 'car', 'bird', 'cat', 'deer', 'dog', 'frog', 'horse', 'ship', 'truck')

Step 2: Define the CNN model

Next, let’s define a simple CNN model for classification:

import torch.nn as nn
import torch.nn.functional as F

class Net(nn.Module):
    def __init__(self):
        super(Net, self).__init__()
        self.conv1 = nn.Conv2d(3, 6, 5)
        self.pool = nn.MaxPool2d(2, 2)
        self.conv2 = nn.Conv2d(6, 16, 5)
        self.fc1 = nn.Linear(16 * 5 * 5, 120)
        self.fc2 = nn.Linear(120, 84)
        self.fc3 = nn.Linear(84, 10)

    def forward(self, x):
        x = self.pool(F.relu(self.conv1(x)))
        x = self.pool(F.relu(self.conv2(x)))
        x = x.view(-1, 16 * 5 * 5)
        x = F.relu(self.fc1(x))
        x = F.relu(self.fc2(x))
        x = self.fc3(x)
        return x

net = Net()

Step 3: Define the loss function and optimizer

Next, we will define the loss function and the optimizer for training the model:

import torch.optim as optim

criterion = nn.CrossEntropyLoss()
optimizer = optim.SGD(net.parameters(), lr=0.001, momentum=0.9)

Step 4: Train the model

Now, let’s train the model on the CIFAR-10 dataset:

for epoch in range(2):  # loop over the dataset multiple times

    running_loss = 0.0
    for i, data in enumerate(trainloader, 0):
        inputs, labels = data

        optimizer.zero_grad()

        outputs = net(inputs)
        loss = criterion(outputs, labels)
        loss.backward()
        optimizer.step()

        running_loss += loss.item()
        if i % 2000 == 1999:    # print every 2000 mini-batches
            print('[%d, %5d] loss: %.3f' %
                  (epoch + 1, i + 1, running_loss / 2000))
            running_loss = 0.0

print('Finished Training')

Step 5: Test the model

Finally, let’s test the model on the test set and calculate the accuracy:

correct = 0
total = 0
with torch.no_grad():
    for data in testloader:
        images, labels = data
        outputs = net(images)
        _, predicted = torch.max(outputs, 1)
        total += labels.size(0)
        correct += (predicted == labels).sum().item()

print('Accuracy of the network on the 10000 test images: %d %%' % (
    100 * correct / total))

This concludes our tutorial on building a classification model using PyTorch on the CIFAR-10 dataset. Feel free to experiment with different architectures and hyperparameters to improve the performance of the model. Happy coding!