Convolutional Neural Networks (CNNs) are a class of deep neural networks that have been widely used in image and video processing tasks due to their ability to capture spatial hierarchy of features from input data. This guide provides an overview of CNN architecture and key concepts.
Key Components of CNN
Convolutional Layers:
- These layers apply various filters (kernels) to the input to extract features.
Activation Function:
- Typically, a Rectified Linear Unit (ReLU) is used to introduce non-linearity to the model.
Pooling Layers:
- These layers reduce the spatial dimensions of the input by pooling operations like max pooling or average pooling.
Fully Connected Layers:
- In the final stage, the features extracted by the convolutional and pooling layers are fed into fully connected layers to make predictions.
CNN Architecture Variants
LeNet-5:
- One of the earliest CNN architectures, which consists of two convolutional layers followed by pooling layers and three fully connected layers.
- More on LeNet-5
AlexNet:
- Introduced the concept of using batch normalization and a larger filter size in CNNs, which significantly improved the performance of CNNs.
- More on AlexNet
VGG:
- Known for its simplicity and effectiveness, VGG uses a series of stacked convolutional and pooling layers without any fully connected layers.
- More on VGG
ResNet:
- Introduced the concept of residual learning, which helps to avoid vanishing gradients in deep networks and improves the performance of CNNs.
- More on ResNet
Conclusion
CNN architectures have become the standard for image and video processing tasks due to their effectiveness and versatility. Understanding the key components and architecture variants can help you choose the right CNN for your specific needs.