Convolutional Neural Networks (CNNs) are a class of deep neural networks that are particularly effective for image processing and computer vision tasks. They have become the go-to model for various applications such as image recognition, object detection, and natural language processing with images.

CNN Layers

A typical CNN architecture consists of several types of layers, each with its specific role in processing the data.

Convolutional Layers

The primary function of convolutional layers is to extract features from the input image. They perform convolution operations using a set of learnable filters (kernels).

  • Convolution Operation: The filters slide over the input image, and at each position, a dot product is computed between the filter and the portion of the image it covers. The result is a feature map.
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<img src="https://cloud-image.ullrai.com/q/convolutional_layer/" alt="Convolutional Layer Example"/>
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Pooling Layers

Pooling layers reduce the spatial dimensions (width and height) of the input volume for the next convolutional layer. They help in reducing computational complexity, parameter, and memory usage.

  • Max Pooling: It selects the maximum element from the region of the feature map.
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<img src="https://cloud-image.ullrai.com/q/max_pooling/" alt="Max Pooling Example"/>
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Fully Connected Layers

After several convolutional and pooling layers, the high-level reasoning in the neural network is done via fully connected layers.

  • Dense Layer: All neurons in a fully connected layer are connected to all activations in the previous layer.
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<img src="https://cloud-image.ullrai.com/q/fully_connected_layer/" alt="Fully Connected Layer Example"/>
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Common CNN Architectures

There are several popular CNN architectures that have been used for various tasks:

  • LeNet-5: The first convolutional neural network designed for hand-written digit recognition.
  • AlexNet: Improved image recognition performance with deeper and wider networks, introduced batch normalization and dropout.
  • VGG: Known for its simplicity and depth, used as a base for various image recognition tasks.
  • ResNet: Introduced the concept of residual learning, allowing for the training of very deep networks.
  • Inception: Proposed the concept of multi-scale feature extraction with multiple convolutional layers and pooling operations.

For more detailed information about these architectures, you can refer to the following links: