types of cnn
Exploring Different Types of Convolutional Neural Networks
types of cnn
Convolutional Neural Networks (CNNs) are specialized deep learning architectures designed primarily for processing structured grid data, such as images. There are several types of CNNs, each tailored for specific tasks or enhancing certain aspects of learning. The most common type is the standard CNN, which utilizes a series of convolutional layers followed by pooling layers to extract features from images, eventually culminating in fully connected layers for classification. Variations include LeNet, an early model designed for digit recognition; AlexNet, known for its success in the ImageNet competition which popularized deep learning; VGGNet, characterized by its deeper architecture and smaller filters; GoogLeNet with its inception modules that allow for multi-scale feature extraction; and ResNet, which introduces skip connections to enable training deeper networks without losing performance. Other adaptations exist for specific purposes, such as Fully Convolutional Networks (FCNs) for semantic segmentation, and YOLO (You Only Look Once) and SSD (Single Shot MultiBox Detector) for real-time object detection. Each type leverages different architectures and techniques to improve accuracy, efficiency, or feature representation, adhering to the diverse needs of computer vision tasks.
To Download Our Brochure: https://www.justacademy.co/download-brochure-for-free
Message us for more information: +91 9987184296
1 - Basic CNN: A standard architecture that uses convolutional layers, pooling layers, and fully connected layers. It's the foundation for more advanced CNN architectures and is widely used in image classification tasks.
2) LeNet 5: One of the earliest CNN architectures developed by Yann LeCun for handwritten digit recognition. It consists of two convolutional layers followed by pooling layers and has fully connected layers at the end.
3) AlexNet: Introduced by Alex Krizhevsky, this architecture significantly improved image classification performance in the 2012 ImageNet competition. It employs ReLU activation, data augmentation, and dropout to combat overfitting.
4) VGGNet: Created by the Visual Geometry Group, VGGNet is known for its simplicity and uniform architecture. It consists of a series of convolutional layers with small 3x3 filters, followed by max pooling layers.
5) GoogLeNet (Inception): This architecture introduced the inception module, which applies multiple types of filters simultaneously (1x1, 3x3, 5x5) and concatenates their outputs. This encourages deeper structures without significantly increasing computational cost.
6) ResNet (Residual Networks): ResNet utilizes skip connections that allow gradients to pass through the network more effectively, enabling the training of very deep networks (even hundreds of layers). It won the ImageNet competition in 2015.
7) DenseNet (Densely Connected Convolutional Networks): DenseNet connects each layer to every subsequent layer, which encourages feature reuse and helps with gradient flow, allowing for efficient training of deep networks.
8) MobileNet: Designed for mobile and embedded vision applications, MobileNet uses depthwise separable convolutions to reduce the computational requirements and model size while maintaining accuracy.
9) EfficientNet: This family of models scales width, depth, and resolution of the network dimensions systematically, achieving high accuracy with far fewer parameters than previous CNNs.
10) U Net: Primarily used in biomedical image segmentation, U Net has a contracting path to capture context and a symmetric expanding path for precise localization, thus producing high quality segmentation results.
11) Faster R CNN: An object detection architecture that combines region proposal networks (RPN) with Fast R CNN for improved detection accuracy. It allows for end to end training, optimizing both the RPN and object detection network together.
12) YOLO (You Only Look Once): A real time object detection system that divides images into a grid and predicts bounding boxes and class probabilities directly from full images in a single evaluation, making it extremely fast.
13) R FCN (Region based Fully Convolutional Networks): Similar to Faster R CNN, R FCN uses position sensitive score maps to achieve high accuracy in object detection while retaining speed by running the prediction in a fully convolutional manner.
14) SqueezeNet: A lightweight architecture designed to maintain accuracy while reducing model size significantly. It uses ‘fire modules’ that squeeze the input through 1x1 convolutions before expanding it with larger convolutions.
15) Capsule Networks: An advanced architecture that aims to overcome limitations of traditional CNNs by using capsules that capture the spatial relationship of features, enabling better generalization in recognizing patterns across various viewpoints.
16) SegNet: A CNN architecture specifically designed for semantic segmentation tasks. It consists of an encoder decoder structure to map input images to pixel wise labels, providing detailed segmentation maps.
17) Xception: An extension of the Inception architecture that introduces depthwise separable convolutions, aiming to improve the representation power while reducing parameters and computations.
This structured overview of CNN types can provide students with knowledge about the evolution, functionality, and applications of different CNN architectures, facilitating their training in the domain of deep learning and computer vision.
Browse our course links : https://www.justacademy.co/all-courses
To Join our FREE DEMO Session: Click Here
Contact Us for more info:
- Message us on Whatsapp: +91 9987184296
- Email id: info@justacademy.co
Flutter Training in Kalyan Dombivali
