Computer Vision Lesson 30 – CNN Architectures | Dataplexa

CNN Architectures Overview

Until now, you have learned individual building blocks of a Convolutional Neural Network: convolutions, pooling, and feature maps.

In this lesson, we zoom out and look at the full structure — how these blocks are arranged into complete CNN architectures.

Understanding architecture is what separates “someone who knows CNN concepts” from “someone who can design and debug models”.

What Is a CNN Architecture?

A CNN architecture is the overall layout of layers in a model.

It defines:

How many layers exist
What type of layers are used
In what order layers are connected
How information flows from input to output

Different architectures solve different vision problems.

Basic CNN Architecture Pattern

Most CNNs follow a repeating pattern:

Convolution
Activation (ReLU)
Pooling

This pattern is stacked multiple times.

Finally, the network ends with:

Flattening
Fully Connected (Dense) layers
Output layer

High-Level CNN Flow

At a high level, CNNs work like this:

Extract low-level features (edges)
Combine them into shapes
Combine shapes into object parts
Make a final decision

Each stage corresponds to deeper layers.

Why CNNs Are Deep

Depth allows CNNs to build understanding gradually.

A shallow network:

Sees only simple patterns
Cannot combine features effectively

A deep network:

Builds hierarchical features
Understands complex structures

Depth is what gives CNNs power.

Typical CNN Layer Arrangement

Example Architecture:

Input Image (224 × 224 × 3)
Conv + ReLU
Conv + ReLU
Pooling
Conv + ReLU
Pooling
Flatten
Dense Layers
Output

This pattern appears in most real CNNs.

Convolution Blocks

Modern CNNs use blocks instead of single layers.

A convolution block usually contains:

Convolution
Activation
Optional normalization

Blocks make networks:

More stable
Easier to design
More reusable

Pooling Placement Matters

Pooling layers reduce spatial size.

Architectures carefully decide:

When to pool
How aggressively to downsample

Too much pooling early:

Loses fine details

Too little pooling:

Increases computation

Fully Connected Layers in CNNs

After feature extraction, CNNs switch to decision-making.

Fully connected layers:

Interpret extracted features
Combine information globally
Produce final predictions

Modern architectures often reduce the number of dense layers to avoid overfitting.

Classification vs Feature Extraction Architectures

Some CNNs are designed to:

Classify images directly

Others are designed to:

Extract features for other tasks
Serve as backbone networks

This idea becomes important in transfer learning.

Why Many CNN Architectures Exist

There is no single “best” CNN architecture.

Different architectures optimize for:

Accuracy
Speed
Memory usage
Real-time performance

This is why models like AlexNet, VGG, ResNet, and MobileNet exist.

Is This Theory or Coding?

This lesson is architectural understanding.

You are learning:

How CNNs are structured
Why layers are arranged in certain ways
How design choices affect performance

Next lessons will connect these ideas to real architectures and code.

Practice Questions

Q1. What is a CNN architecture?

The overall layout and organization of layers in a CNN.

Q2. Why are CNNs deep?

Depth allows hierarchical feature learning from simple to complex patterns.

Q3. What role do fully connected layers play?

They interpret extracted features and make final predictions.

Design Thinking Exercise

Imagine building a CNN for:

Handwritten digit recognition
Face recognition
Self-driving car vision

Each task requires a different architecture depth and complexity.

Quick Recap

CNN architecture defines layer organization
Convolution blocks extract features
Pooling controls spatial size
Dense layers make decisions
Different tasks require different designs

Next lesson: Transfer Learning — using pre-trained CNN architectures effectively.

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