Convolutional Neural Networks (CNNs) are a class of deep neural networks that are particularly effective for processing data with a grid-like topology, such as images, video, and time-series data. They are widely used in computer vision tasks due to their ability to automatically and adaptively learn spatial hierarchies of features from input images.
Key Concepts
- Convolutional Layers: These layers apply various filters (also known as kernels) to the input data to extract features.
- Activation Functions: Commonly used activation functions include ReLU (Rectified Linear Unit), sigmoid, and tanh.
- Pooling Layers: These layers reduce the spatial dimensions of the input volume for the next convolutional layer.
CNN Architecture
A typical CNN architecture consists of the following layers:
- Input Layer: The input to the network is usually an image.
- Convolutional Layers: Multiple convolutional layers with increasing depth and complexity.
- Pooling Layers: After each convolutional layer, a pooling layer is applied to reduce the spatial dimensions.
- Fully Connected Layers: The final fully connected layers perform classification or regression tasks.
- Output Layer: The output layer provides the final prediction.
Example of CNN Architecture
- Input Layer: 32x32x3 image
- Convolutional Layer 1: 32 filters, 5x5 kernel, stride 1, padding 0
- ReLU Activation
- Pooling Layer 1: 2x2 pooling, stride 2
- Convolutional Layer 2: 64 filters, 5x5 kernel, stride 1, padding 0
- ReLU Activation
- Pooling Layer 2: 2x2 pooling, stride 2
- Flatten Layer: Flatten the 3D output to 1D
- Fully Connected Layer 1: 128 units
- ReLU Activation
- Fully Connected Layer 2: 10 units (for classification tasks)
- Output Layer: Softmax activation
Applications
CNNs have been successfully applied to various computer vision tasks, such as:
- Image classification
- Object detection
- Image segmentation
- Video recognition
For more information on CNN applications, visit our CNN Applications.
Further Reading
Convolutional Neural Network