I’ve been thinking a lot about image classification lately. It’s fascinating how computers can learn to recognize patterns in images, and I wanted to share my approach to building a custom CNN using PyTorch. This isn’t just theoretical—it’s something I’ve implemented and refined through practice.

Have you ever wondered how machines actually learn to distinguish between different objects in images? Let me walk you through the process.

We start with data preparation. The CIFAR-10 dataset gives us 60,000 small color images across 10 categories. But raw data isn’t enough—we need to transform it to help our model learn better. Here’s how I set up data augmentation:

train_transforms = transforms.Compose([
    transforms.RandomHorizontalFlip(p=0.5),
    transforms.RandomRotation(degrees=15),
    transforms.ColorJitter(brightness=0.2, contrast=0.2),
    transforms.ToTensor(),
    transforms.Normalize(mean=[0.4914, 0.4822, 0.4465], 
                        std=[0.2470, 0.2435, 0.2616])
])

Why do we need these transformations? They help our model become robust to variations it might see in real-world images.

Now, let’s build the CNN architecture. I’ve found that a balanced approach works best—not too simple, not too complex. Here’s a structure that has served me well:

class CustomCNN(nn.Module):
    def __init__(self, num_classes=10):
        super().__init__()
        self.features = nn.Sequential(
            nn.Conv2d(3, 32, 3, padding=1),
            nn.BatchNorm2d(32),
            nn.ReLU(),
            nn.MaxPool2d(2),
            
            nn.Conv2d(32, 64, 3, padding=1),
            nn.BatchNorm2d(64),
            nn.ReLU(),
            nn.MaxPool2d(2),
            
            nn.Conv2d(64, 128, 3, padding=1),
            nn.BatchNorm2d(128),
            nn.ReLU(),
            nn.MaxPool2d(2)
        )
        
        self.classifier = nn.Sequential(
            nn.Linear(128 * 4 * 4, 512),
            nn.ReLU(),
            nn.Dropout(0.5),
            nn.Linear(512, num_classes)
        )

Notice how I use batch normalization after each convolutional layer? This helps stabilize training and often leads to better performance. The dropout layer in the classifier prevents overfitting—something I learned the hard way through trial and error.

Training this model requires careful tuning. I use Adam optimizer with a learning rate that decreases over time:

model = CustomCNN().to(device)
optimizer = optim.Adam(model.parameters(), lr=0.001)
scheduler = optim.lr_scheduler.StepLR(optimizer, step_size=30, gamma=0.1)
criterion = nn.CrossEntropyLoss()

What happens if the learning rate is too high? The model might never converge properly. Too low, and training takes forever. Finding that sweet spot is crucial.

During training, I monitor both training and validation accuracy. If they start diverging too much, it’s a sign of overfitting. Early stopping has saved me many times from training models that perform well only on the training data.

Evaluation is where we see the real results. After training, I test on unseen data and look at the confusion matrix to understand where the model struggles. Sometimes the patterns are surprising—maybe it confuses dogs with cats more often than you’d expect.

The beauty of this approach is its flexibility. Once you understand the components, you can adapt them to different problems. Want to work with medical images? Satellite data? The principles remain the same.

I encourage you to experiment with different architectures and hyperparameters. Change the number of layers, try different activation functions, or adjust the dropout rates. Each project teaches you something new.

What questions do you have about implementing your own CNN? I’d love to hear about your experiences and challenges. If you found this helpful, please share it with others who might benefit, and feel free to leave comments with your thoughts or questions.