Are CNNs still relevant with Vision Transformers?

Yes. CNNs remain efficient, especially for smaller models and edge deployment. Transformers excel at scale but CNNs often win on speed and resource efficiency. Hybrids combine both.

How deep should a CNN be?

Deeper isn't always better. ResNet showed very deep nets (100+ layers) can work with skip connections. For most tasks, established architectures (ResNet-50, EfficientNet-B0) are good starting points.

Can CNNs work on non-image data?

Yes! CNNs work on any grid-like data: audio spectrograms, time series (as 1D convolutions), graphs (with specialised layers). The core idea of local pattern detection transfers.

What's the computational cost of CNNs?

Varies hugely by architecture. Mobile-optimised models (MobileNet) run on phones. Large models (EfficientNet-B7) need powerful GPUs. Always profile for your deployment target.

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Convolutional Neural Network

CNN

Neural network architecture designed for processing grid-like data such as images. Uses convolution operations to automatically learn spatial features and patterns.

In-Depth Explanation

Convolutional Neural Networks (CNNs) revolutionised computer vision by learning to recognise visual patterns directly from pixels. They remain fundamental to image processing AI.

CNN key components:

Convolutional layers: Learn local patterns (edges, textures)
Pooling layers: Reduce spatial dimensions, add invariance
Fully connected layers: Combine features for final output
Activation functions: Add non-linearity (ReLU)

Why convolutions work for images:

Local connectivity: Pixels near each other are related
Weight sharing: Same filter across entire image
Translation invariance: Recognise patterns anywhere
Hierarchical features: Simple → complex patterns

Landmark CNN architectures:

LeNet (1998): Pioneer for digit recognition
AlexNet (2012): ImageNet breakthrough
VGG (2014): Deeper is better
ResNet (2015): Skip connections enable very deep nets
EfficientNet (2019): Balanced scaling

Business Context

CNNs are the workhorse of image AI. From quality inspection to document OCR, most visual AI applications use CNN-based architectures or their derivatives.