Classic Convolutional Neural Networks (CNNs) Classic CNNs are a family of neural networks widely used in computer vision and image processing due to their e...
Classic Convolutional Neural Networks (CNNs)
Classic CNNs are a family of neural networks widely used in computer vision and image processing due to their effectiveness in extracting visual features from data. These models consist of a stack of convolutional layers followed by max-pooling layers, which collectively learn to identify patterns and relationships in the image data.
AlexNet
The first CNN model was AlexNet, introduced in 2012 by Alex Krizhevsky.
It consists of a hierarchical structure with multiple convolutional and max-pooling layers.
AlexNet achieved remarkable performance on the ImageNet database, demonstrating its effectiveness for object recognition tasks.
VGG
VGG (Visual Geometry Group) is a family of CNNs introduced in 2014.
It consisted of various models with varying numbers of layers, each with different filter sizes and pooling operations.
VGG achieved significant improvements in accuracy and performance on image classification tasks.
ResNet
ResNet (Resampling-based Convolutional Network) was introduced in 2015.
It introduced a new strategy called 'skip connections' that connect different layers in the network.
ResNet achieved even higher accuracy and performance than VGG on various image classification and segmentation tasks.
Inception
Inception is a family of CNNs introduced in 2015 that explored deeper representations of data using multiple layers and 3D filters.
It introduced a new pooling operation called 'max-pooling with kernel size 3x3' and introduced concepts like 'depthwise' and 'pointwise' convolutions.
Inception achieved remarkable results in image classification, object detection, and semantic segmentation tasks