Viso.ai

DECEMBER 10, 2023

Get a personalized demo. Training of Neural Networks for Image Recognition The images from the created dataset are fed into a neural network algorithm. The training of an image recognition algorithm makes it possible for convolutional neural network image recognition to identify specific classes.

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Viso.ai

DECEMBER 1, 2024

Get a demo. Automatic image-based plant disease severity estimation using Deep convolutional neural network (CNN) applications was developed, for example, to identify apple black rot. Thus, Convolutional Neural Networks automatically infer the required pose information and detect athletes’ swimming styles.

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Viso.ai

FEBRUARY 25, 2024

To learn more, book a demo. Following that, the development of Convolutional Neural Networks (CNNs) was a watershed moment in the field. The introduction of the Super-Resolution Convolutional Neural Network (SRCNN) later demonstrated that deep learning models could outperform traditional image resolution methods.

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Viso.ai

MARCH 12, 2024

Today’s boom in computer vision (CV) started at the beginning of the 21 st century with the breakthrough of deep learning models and convolutional neural networks (CNN). Book a demo to learn more about how Viso Suite can help solve business problems. SURF showed strong performance – SURF-128 with an 85.7%

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Viso.ai

FEBRUARY 23, 2024

Get a demo. Interactive Segmentation – Source Popular Image Segmentation Models Mask-RCNN The Mask Region-based Convolutional Neural Network (RCNN) was one of the most popular segmentation algorithms during Computer Vision’s early days. Book a demo to learn more about the Viso Suite.

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Viso.ai

OCTOBER 7, 2024

Object Detection : Computer vision algorithms, such as convolutional neural networks (CNNs), analyze the images to identify and classify waste types (i.e., Convolutional neural network-based systems often require expensive hardware and consume high amounts of energy. plastic, metal, paper).

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Viso.ai

FEBRUARY 11, 2024

2015 – Microsoft researchers report that their Convolutional Neural Networks (CNNs) exceed human ability in pure ILSVRC tasks. Object Detection and Instance Segmentation – DeepMAD: Mathematical Architecture Design for Deep Convolutional Neural Network, published by Xuan Shen et al.,

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Viso.ai

OCTOBER 10, 2024

To get started with Viso Suite, book a demo with our team of experts. We will elaborate on computer vision techniques like Convolutional Neural Networks (CNNs). They applied clustering in combination with deep neural networks to provide pseudo-labels for a convolutional neural network.

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Viso.ai

AUGUST 5, 2024

It uses a Region Proposal Network (RPN) and Convolutional Neural Networks (CNNs) to identify and locate objects in complex real-world images. Get a demo. Convolution Neural Network (CNN) A Convolutional Neural Network is a type of deep neural network that detects objects in the image.

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Viso.ai

APRIL 26, 2024

Object detection systems typically use frameworks like Convolutional Neural Networks (CNNs) and Region-based CNNs (R-CNNs). Concept of Convolutional Neural Networks (CNN) However, in prompt object detection systems, users dynamically direct the model with many tasks it may not have encountered before.

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Viso.ai

NOVEMBER 27, 2024

Multiple machine-learning algorithms are used for object detection, one of which is convolutional neural networks (CNNs). This article will explore the entire YOLO family, we will start from the original to the latest, exploring their architecture, use cases, and demos. To learn more, book a demo with our team.

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The MLOps Blog

NOVEMBER 14, 2023

Example of a deep learning visualization: small convolutional neural network CNN, notice how the thickness of the colorful lines indicates the weight of the neural pathways | Source How is deep learning visualization different from traditional ML visualization? You can find an interactive version online.

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Viso.ai

SEPTEMBER 1, 2024

To get started, book a demo with our team of experts. Get Started With Enterprise-Grade Computer Vision To start using Viso Suite for your AI initiatives, book a demo with our team. With a platform that covers all stages of the application development lifecycle. We’ll discuss your use case and how Viso Suite can help solve it.

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Mlearning.ai

MARCH 10, 2023

A Haar-Feature is just like a kernel in convolutional neural-network (CNN), except that in a CNN, the values of the kernel are determined by training, while a Haar-Feature is manually determined. It is a machine learning based approach where a cascade function is trained from a lot of positive and negative images.

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Viso.ai

FEBRUARY 25, 2023

Vision Transformer (ViT) have recently emerged as a competitive alternative to Convolutional Neural Networks (CNNs) that are currently state-of-the-art in different image recognition computer vision tasks. Get a demo for your company. Use Cases and applications of Vision Transformers About us: Viso.ai

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Convolutional Neural Networks Computer Vision Neural Network Natural Language Processing 52

Viso.ai

AUGUST 1, 2024

To learn more about how Viso Suite can help automate your business needs, book a demo with our team. Feature Extraction with a Convolutional Neural Network (CNN): In this first step of the process, DensePose passes the given image into a pre-trained Convolutional Neural Network (CNN), such as ResNet.

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Viso.ai

DECEMBER 20, 2023

Get a demo for your organization. However, unsupervised learning has its own advantages, such as being more resistant to overfitting (the big challenge of Convolutional Neural Networks ) and better able to learn from complex big data, such as customer data or behavioral data without an inherent structure. About us: Viso.ai

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Viso.ai

FEBRUARY 25, 2023

Get a personal demo. Deep learning-based segmentation: Deep learning techniques, such as Convolutional Neural Networks (CNNs), have revolutionized image segmentation by providing highly accurate and efficient solutions. Read more about the basics of a Convolutional Neural Network.

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Viso.ai

FEBRUARY 21, 2024

Book a demo with us to learn more. This is what makes them different from matrices used in Convolutional Neural Networks (CNNs). Deep Graph Convolutional Neural Network II (DGCNNII) This architecture uses a deep graph convolutional neural network architecture for graph classification.

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Viso.ai

JUNE 19, 2024

Get your demo here! However, when deep learning became popular in the 2010s, DeepPose was introduced by researchers at Facebook in 2014, this was an inspirational model that utilized Convolutional Neural Networks ( CNNs ) to effectively detect human poses directly from images.

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Viso.ai

FEBRUARY 8, 2024

Get a demo here. Viso Suite is the end-to-end, no-code computer vision platform Idea Behind Deep Belief Networks Deep belief networks consist of various layers of neurons, each connected to the neuron of the subsequent layer. Book a demo to learn more about the Viso suite.

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Viso.ai

DECEMBER 22, 2023

The Segment Anything Model Technical Backbone: Convolutional, Generative Networks, and More Convolutional Neural Networks (CNNs) and Generative Adversarial Networks (GANs) play a foundational role in the capabilities of SAM. This is essential for its high accuracy and efficiency in image segmentation.

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Viso.ai

MAY 31, 2024

Get a demo here. Deep Learning Approaches Convolutional Neural Networks (CNNs) : The CNNs including AlexNet , VGGNet , and ResNet helped solve computer vision problems by learning the hierarchal features directly from the Pascal VOC data. To see what Viso Suite can do for you, book a demo with our team.

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Viso.ai

JULY 17, 2024

An image can be represented by the relationships between the activations of features detected by a convolutional neural network (CNN). To learn more about solving business challenges with computer vision, book a demo with our team of experts. A Gram matrix captures the style information of an image in numerical form.

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Viso.ai

OCTOBER 10, 2024

Get a demo for your organization. The most popular and successful form of machine learning using neural networks is deep learning, which applies deep convolutional neural networks ( CNN ) to solve classification tasks. Pattern Recognition Projects and Use Cases About us: viso.ai

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Viso.ai

DECEMBER 18, 2023

Get a demo. YOLO’s architecture was a significant revolution in the real-time object detection space, surpassing its predecessor – the Region-based Convolutional Neural Network (R-CNN). The neck merges these feature maps using path aggregation blocks like the Feature Pyramid Network (FPN).

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Viso.ai

SEPTEMBER 2, 2024

To learn more about enterprise-grade AI, book a demo with our team of experts to discuss Viso Suite. To learn more, book a demo with our team of experts. About us : Viso Suite provides firms with an industry-agnostic, comprehensive solution to business challenges – computer vision infrastructure.

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Viso.ai

FEBRUARY 19, 2024

To learn more, book a demo with the Viso team. To overcome this IP concern – researchers have applied a Convolutional Neural Network (CNN) to detect plagiarized text and images as well as problematic deepfakes on the internet. About us: Viso Suite is the end-to-end computer vision solution for enterprises.

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Viso.ai

MARCH 20, 2024

Learn more about Viso Suite by booking a demo with us. Foundation models are large-scale neural network architectures that undergo pre-training on vast amounts of unlabeled data through self-supervised learning. Thus, eliminating the need for time-consuming, complex point solutions.

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Viso.ai

JANUARY 4, 2024

Get a demo. At its core, Semantic Segmentation is driven by deep learning models , particularly Convolutional Neural Networks (CNNs) , acting as an encoder and decoder. About Us: Viso Suite is the no-code, end-to-end platform that enables businesses to use real-world computer vision. What is Segmentation?

Computer Vision 52

Computer Vision Convolutional Neural Networks Deep Learning Neural Network 52

Viso.ai

JUNE 10, 2024

Book a demo with our team of experts to learn more. Feature Extraction with Deep Learning Convolutional Neural Networks ( CNNs ) can be used to extract features such as edges, corners, and textures from images or video frames. Find out more about Viso Suite by booking a demo with our team of experts.

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Viso.ai

JANUARY 16, 2024

Get a demo here. Matching Networks: The algorithm computes embeddings using a support set, and one-shot learns by classifying the query data sample based on which support set embedding is closest to the query embedding – source. The embedding functions can be convolutional neural networks (CNNs).

Neural Network 52

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Viso.ai

APRIL 4, 2024

Deep Learning with Convolutional Neural Network – Source For example, image classification models use the image’s RGB values to produce classes with a confidence score. Training that network is about minimizing a loss function. To learn more, book a demo with our team.

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Viso.ai

JUNE 25, 2024

Get a personal demo. DeepLab v1 DeepLab introduces several key innovations for image segmentation , but one of the most important is the use of Atrous convolution (Dilated Convolution). However, DeepLab v1 replaces the final fully connected layers in VGG-16 with convolutional layers and utilizes atrous convolutions.

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Viso.ai

OCTOBER 15, 2024

In the field of real-time object identification, YOLOv11 architecture is an advancement over its predecessor, the Region-based Convolutional Neural Network (R-CNN). Using an entire image as input, this single-pass approach with a single neural network predicts bounding boxes and class probabilities. Redmon, et al.

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Viso.ai

MAY 6, 2024

To learn more, get a personalized demo from the Viso team. In particular, researchers are working with the following deep learning models to enhance spatio-temporal action recognition systems: Convolutional Neural Networks (CNNs) In a basic sense, spatial recognition systems use CNNs to extract features from pixel data.

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Viso.ai

JULY 9, 2024

Learn more and book a demo with us. Neural Networks For now, most attempts to develop ASI are still grounded in well-known models, such as neural networks , machine learning/deep learning , and computational neuroscience. About us: Viso Suite is the only end-to-end computer vision infrastructure.

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