A colleague recently asked me how to start with image recognition. They had heard terms like “neural networks” and “deep learning,” but the step from theory to a working model seemed vast. That conversation is why I’m writing this. I want to show you that building your own image classifier from scratch is not just possible; it’s a clear, structured process. Let’s do it together, and by the end, you’ll have a model that can tell a cat from a car. If you find this useful, I encourage you to share it with someone else who might be starting their journey.

Think of a Convolutional Neural Network (CNN) as a very diligent, multi-layered inspector. It doesn’t look at an entire image at once. Instead, it scans small sections at a time, looking for basic patterns like edges or color blobs in the first layer. Subsequent layers combine these simple patterns to recognize more complex features—like a whisker, then an eye, then finally a face. This local, hierarchical inspection is what makes CNNs so powerful for images.

I use PyTorch for this work because it feels intuitive. Its design is Pythonic, letting you build and adjust your network dynamically, almost like you’re writing a regular script. This makes experimentation and debugging much more straightforward. Are you ready to see what that looks like in code?

First, we need our tools and data. We’ll use the CIFAR-10 dataset, a classic collection of 60,000 small, 32x32 pixel images across 10 categories like airplanes, dogs, and trucks.

import torch
import torch.nn as nn
import torch.optim as optim
import torchvision.transforms as transforms
from torchvision.datasets import CIFAR10
from torch.utils.data import DataLoader

# Basic transforms to prepare image data
transform = transforms.Compose([
    transforms.ToTensor(), # Converts image to numbers (tensor)
    transforms.Normalize((0.5, 0.5, 0.5), (0.5, 0.5, 0.5)) # Scales pixel values
])

# Load the dataset
train_set = CIFAR10(root='./data', train=True, download=True, transform=transform)
train_loader = DataLoader(train_set, batch_size=32, shuffle=True)

With data ready, we define the CNN’s architecture. This is where you get to be an architect. How many layers? What size filters? I’ll show you a simple but effective structure.

class SimpleCNN(nn.Module):
    def __init__(self):
        super().__init__()
        # Convolutional layers: extract features
        self.conv1 = nn.Conv2d(3, 16, 3, padding=1) # Input: 3 color channels, Output: 16 feature maps
        self.pool = nn.MaxPool2d(2, 2)               # Downsamples the image
        self.conv2 = nn.Conv2d(16, 32, 3, padding=1)
        
        # Fully connected layers: make the classification decision
        self.fc1 = nn.Linear(32 * 8 * 8, 128)       # 32*8*8 comes from the image dimensions after pooling
        self.fc2 = nn.Linear(128, 10)                # 10 output classes for CIFAR-10
        
    def forward(self, x):
        x = self.pool(torch.relu(self.conv1(x)))
        x = self.pool(torch.relu(self.conv2(x)))
        x = torch.flatten(x, 1) # Flatten for the linear layer
        x = torch.relu(self.fc1(x))
        x = self.fc2(x)
        return x

model = SimpleCNN()
print(model)

Notice the forward function. This defines the path our data takes through the network. We apply a convolution, then a ReLU activation function to introduce non-linearity, then pooling. This sequence repeats. But how does the model learn from its mistakes? That’s where training comes in.

Training is a cycle of prediction, calculation of error (loss), and adjustment. We use an optimizer to guide those adjustments. Think of it like tuning a radio: the loss tells you how much static there is, and the optimizer turns the dial.

criterion = nn.CrossEntropyLoss()  # Measures how wrong the predictions are
optimizer = optim.Adam(model.parameters(), lr=0.001) # The algorithm that adjusts the weights

# A basic training loop for one epoch
model.train()
for images, labels in train_loader:
    optimizer.zero_grad()     # Clear previous gradients
    outputs = model(images)   # Forward pass: make a prediction
    loss = criterion(outputs, labels) # Calculate error
    loss.backward()           # Backward pass: calculate gradients
    optimizer.step()          # Update weights
    # print(f'Loss: {loss.item()}') # You can print loss to see it decrease

This loop runs for many epochs. Each pass through the data, the model’s weights are nudged in a direction that should reduce future loss. It’s a process of gradual refinement. What do you think happens if the learning rate is too high? The model might overshoot the best weights and never converge properly.

After training, we must evaluate on unseen data—the test set. This tells us if our model has truly learned to generalize or if it just memorized the training examples. Accuracy here is the real test.

Building from scratch teaches you the core mechanics, but in practice, you often don’t start from zero. Transfer learning, using a powerful pre-trained model like ResNet and fine-tuning it for your specific task, is a incredibly effective shortcut. It’s like learning to paint by first studying the masters before developing your own style.

The journey from a blank script to a functioning image classifier is immensely satisfying. You move from abstract concepts to a tangible program that learns from data. Start with this simple CNN, experiment with adding layers or adjusting hyperparameters, and see how the accuracy changes. The best way to learn is to try, break, and fix things. I hope this guide gives you that starting point. If it helped clarify the path, please like this article, share it with your network, and leave a comment below about what you built. I’d love to hear about your projects.