About This Architecture

VGG-style CNN architecture for image classification processes 224×224×3 RGB images through four convolutional blocks with progressively deeper filters (32→64→128→256), each followed by ReLU activation and max pooling for spatial dimension reduction. Feature extraction flows through stacked Conv layers with batch normalization and padding=same, culminating in a flattened 50,176-unit feature map that feeds into two fully connected layers with dropout regularization. The classifier outputs 1000 class predictions via softmax, demonstrating how hierarchical convolution captures low-level edges in early blocks and high-level semantic features in deeper blocks. Fork this diagram on Diagrams.so to customize filter counts, adjust input resolution, or adapt the architecture for your specific dataset and class count. This pattern balances computational efficiency with strong feature learning, making it ideal for transfer learning and fine-tuning on custom image datasets.

People also ask

How does a CNN extract features from images and classify them using convolutional and fully connected layers?

This CNN architecture progressively extracts features through 4 convolutional blocks (Conv1–Conv4) with increasing filter depth (32→256), applying ReLU activation and max pooling to reduce spatial dimensions while preserving semantic information. The flattened feature map (14×14×256 = 50,176 units) feeds into two fully connected layers with dropout for regularization, culminating in a 1000-unit so