Building a CNN for Image Classification

Convolutional Neural Networks (CNNs) are the backbone of modern computer vision. Learn how to build one using PyTorch to classify handwritten digits from the MNIST dataset.

The CNN Architecture

A typical CNN consists of several types of layers:

Convolutional Layers: Detect local patterns like edges and textures.
Pooling Layers: Reduce spatial dimensions while retaining important features.
Fully Connected Layers: Perform the final classification based on the extracted features.

                    class CNN(nn.Module):

                        def __init__(self):

                            super(CNN, self).__init__()

                            self.conv1 = nn.Conv2d(1, 32, kernel_size=3)

                            self.pool = nn.MaxPool2d(2, 2)

                            self.fc1 = nn.Linear(32 * 13 * 13, 10)

                        def forward(self, x):

                            x = self.pool(F.relu(self.conv1(x)))

                            x = x.view(-1, 32 * 13 * 13)

                            x = self.fc1(x)

                            return x

Training on MNIST

The MNIST dataset contains 70,000 images of handwritten digits. With this simple CNN, you can achieve over 98% accuracy in just a few epochs of training.

Mastered CNNs?

Explore the next frontier: Transformers for Computer Vision (Vision Transformers).

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