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

1. Prepare dataset
2. Define architecture or use pretrained
3. Apply data augmentation
4. Train with proper learning rate
5. Monitor validation metrics
6. Fine-tune if needed

Transfer Learning

Using pretrained models:

1. Load pretrained weights
2. Freeze early layers
3. Add custom head
4. Train on your data
5. 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.