Overview of Siamese U-Net
Siamese U-Net is a machine learning model that is used for data efficient change detection. What does that mean? Let's break it down:
Machine learning is a way for computers to learn from data and make predictions based on that learning. Think of it like teaching a child how to identify different objects. You show them pictures of different objects and tell them what each one is. Over time, the child learns to recognize the objects on their own, without needing you to
SimpleNet is a convolutional neural network that is designed to process image recognition tasks with remarkable accuracy. With 13 layers, it has a homogeneous design which uses 3 × 3 kernels for convolutional operations and 2 × 2 kernels for pooling operations. The design philosophy of SimpleNet is to have a network structure that is simple to understand and implement, while still being highly efficient and accurate.
Benefits of SimpleNet Architecture
The SimpleNet architecture offers a signi
Single-Path NAS is a type of convolutional neural network architecture built using the Single-Path neural architecture search approach. This NAS uses one single-path over-parameterized ConvNet to encode all architectural decisions with shared convolutional kernel parameters. The approach is based on the idea that different candidate convolutional operations in NAS can be viewed as subsets of a single superkernel.
What is Single-Path NAS?
Single-Path NAS is a type of convolutional neural netwo
Introduction to SKNet: A Powerful Convolutional Neural Network
SKNet is a type of convolutional neural network that has been gaining popularity in the field of computer vision. It is particularly useful for image recognition and classification tasks, and has shown impressive results in various benchmarks and competitions.
In this article, we will provide an overview of SKNet, its architecture, and the technology behind it. We will explain what selective kernel units are, how selective kernel c
What is SNet?
SNet is a type of neural network architecture used for object detection in deep learning. Specifically, it is the backbone architecture used in the ThunderNet two-stage object detector, which is one of the latest state-of-the-art object detection models.
How does SNet work?
SNet is a convolutional neural network (CNN) architecture, meaning it is designed to work with image data. In particular, SNet is based on the ShuffleNetV2 architecture, which is known for its small size and
SpineNet: A Scalable Neural Network for Object Detection
If you are familiar with computer vision algorithms, you might have heard of Convolutional Neural Networks (CNNs) before. CNNs are widely used in object detection and recognition tasks. However, the biggest challenge of using these networks is that they require high computational resources, making them difficult to use in real-time applications such as autonomous vehicles, drones or mobile devices.
That's where SpineNet comes in. It is a
Overview of SPP-Net
SPP-Net is a type of neural architecture that uses a method called spatial pyramid pooling to overcome the fixed-size constraint of the network. This allows the network to handle images of different sizes without needing to crop or warp them in advance.
At the heart of SPP-Net is a layer that aggregates information at a deeper stage of the network hierarchy. This layer sits between the convolutional layers and the fully-connected layers. It is called the SPP layer, and it p
What is SqueezeNet, and How Does it Work?
SqueezeNet is a convolutional neural network architecture that is designed to be lightweight with a small number of parameters. This network structure is ideal for use in devices with low computation power like mobile phones, and embedded systems. SqueezeNet aims to reduce the size of the model by employing different design strategies. One of the most notable strategies is the use of fire modules that "squeeze" parameters using 1x1 convolutions.
Convol
SqueezeNeXt is a convolutional neural network based on the architecture of SqueezeNet. However, it incorporates some significant changes to reduce the number of parameters used while improving model accuracy. These changes include a two-stage squeeze module that uses more aggressive channel reduction and separable 3 × 3 convolutions, eliminating the additional 1×1 branch after the squeeze module.
The Design of SqueezeNeXt
SqueezeNeXt is a deep learning neural network architecture that is base
A TResNet is a variation of a ResNet that is designed to improve accuracy while maintaining efficient training and inference using a GPU. This type of network incorporates several design elements, including SpaceToDepth stem, Anti-Alias downsampling, In-Place Activated BatchNorm, Blocks selection, and squeeze-and-excitation layers to achieve its improved performance.
ResNet Basics
Before discussing TResNets, it’s important to understand the basics of ResNets. ResNets (short for residual netwo
The development of neural networks has revolutionized the world of computer science and machine learning. One of the newest architectures is the uNetXST, which is a neural network that is built to take input from multiple tensors and contains spatial transformer units (ST).
What is uNetXST?
uNetXST is a deep neural network architecture that is specifically designed to enable accurate pixel-wise segmentation of images. uNetXST uses a convolutional neural network (CNN) that is trained end-to-en
VGG is a convolutional neural network architecture used in deep learning. It was created to increase the depth of neural networks, which was a major issue in computer vision tasks. The network relies on small 3 x 3 filters and is known for its simplicity as it only uses pooling layers and a fully connected layer.
What is VGG?
VGG is a deep learning architecture used for image recognition tasks. It was introduced in 2014 by a group of researchers at the Visual Geometry Group at the University
VoVNet: A More Efficient Convolutional Neural Network
If you've ever used object recognition software, you've likely benefited from a convolutional neural network (CNN). These AI algorithms are responsible for recognizing images and the objects they contain, and have become crucial components of applications like self-driving cars and facial recognition software. However, one issue with CNNs is that they can be slow and inefficient, which makes them less useful for real-time applications. That'
Introduction to VoVNetV2
VoVNetV2 is a type of convolutional neural network that has been designed to solve problems in computer vision applications. It is an improvement on the previous VoVNet model by using two effective strategies: residual connection, and effective Squeeze-Excitation(eSE). We'll dive deeper into these strategies later on.
Understanding the need for VoVNetV2
The field of computer vision has experienced exponential growth over the past decade, with the rise of deep learnin
WideResNet: A High-Performing Variant on Residual Networks
In recent years, the field of deep learning has seen tremendous progress with the development of convolutional neural networks (CNNs). They have been used in various applications such as image recognition, natural language processing, and speech recognition, to name a few.
One of the most successful deep architectures, ResNets, was introduced in 2015. Since its inception, ResNets have consistently outperformed the previous state-of-the
Xception is a convolutional neural network architecture that is increasingly gaining popularity because of its efficiency and effectiveness. The structure of this neural network is different from other standard convolutional neural networks, as it solely relies on depthwise separable convolution layers, which significantly reduces the computational requirements and memory footprint of the network.
The Need for Xception
Before understanding what Xception is, one first needs to understand the n
Overview of ZFNet
ZFNet is a type of neural network that is used for image recognition tasks. It was originally designed in 2013 by Matthew D. Zeiler and Rob Fergus at New York University. It was created to improve upon an earlier neural network called AlexNet, which was the first neural network to win a large-scale computer vision competition called the ImageNet Challenge.
What is a Convolutional Neural Network?
A convolutional neural network (CNN) is a type of neural network that is used f