Convolutional and pooling layers work together to extract hierarchical features from input images. Early layers detect edges and textures, while deeper layers recognize complex patterns and objects.
🎯 Classification Head
Fully connected layers at the end use extracted features to make final predictions. They combine spatial information into class probabilities or regression outputs.
🏗️ Hierarchical Learning
CNNs learn representations at multiple levels of abstraction. Each layer builds upon the previous layer's features to capture increasingly complex patterns.
Interactive Architecture Builder
Click the buttons below to build your own CNN architecture: