Image Classification with Convolutional Neural Networks

How CNNs Process Images

Convolutional Neural Networks are specifically designed to process grid-like data such as images, using convolution operations to detect features.

CNN Building Blocks

Convolutional Layers

Learn filters/kernels

Detect local patterns

Weight sharing

Translation invariance

Pooling Layers

Reduce dimensions

Max pooling

Average pooling

Increase receptive field

Fully Connected Layers

Classification head

Combine features

Output predictions

Classic Architectures

LeNet-5

Pioneer CNN

Digit recognition

Simple architecture

AlexNet

ImageNet breakthrough

ReLU activation

Dropout regularization

VGG

Deep networks

3x3 convolutions

Transfer learning friendly

ResNet

Residual connections

Very deep networks

Skip connections

EfficientNet

Compound scaling

Efficient architecture

State-of-the-art accuracy

Building a Classifier

Prepare dataset

Define architecture or use pretrained

Apply data augmentation

Train with proper learning rate

Monitor validation metrics

Fine-tune if needed

Transfer Learning

Using pretrained models:

Load pretrained weights

Freeze early layers

Add custom head

Train on your data

Fine-tune as needed

Best Practices

Use data augmentation

Normalize inputs

Use batch normalization

Apply regularization

Use learning rate scheduling

Conclusion

CNNs remain the foundation of image classification, with transfer learning making them accessible for any task.