🧠 Understanding CNN Generalisation with Data Augmentation (TensorFlow – CIFAR-10)

📘 Data Augmentation in CNNs and the impact on Generalisation (Using CIFAR-10 Experiments) Data augmentation is widely used when training convolutional neural networks, especially for image classification tasks. The idea is simple: by transforming training images — rotating, flipping, or shifting them, we can introduce more variation and help the model generalise better. However, one question that is often overlooked is: 👉 Does more augmentation always improve performance? In this post, I investigate how different levels of data augmentation affect a CNN trained on the CIFAR-10 dataset. All experiments, code, and plots shown here are taken directly from my notebook. 📂 Dataset Overview: CIFAR-10 The CIFAR-10 dataset contains: 60,000 colour images 10 output classes 32×32 resolution A balanced distribution across classes One key detail is the image resolution. At 32×32 pixels, fine details are limited, and some classes (like cats and dogs) can look very similar. This becomes important when