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Segment Anything Model (SAM) Deep Dive – Complete 2024 Guide

Viso.ai

Today, the computer vision project has gained enormous momentum in mobile applications, automated image annotation tools , and facial recognition and image classification applications. Convolutional Neural Networks (CNNs) CNNs are integral to the image encoder of the Segment Anything Model architecture.

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How to Create Synthetic Data to Train Deep Learning Algorithms?

Dlabs.ai

These days, with a little ingenuity, you can automate the task. In deep learning, a computer algorithm uses images, text, or sound to learn to perform a set of classification tasks. Say, you want to auto-detect headers in a document. So, by automating the creation of synthetic data, you get two clear benefits.

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Big Medical Image Preprocessing With Apache Beam | A Step-by-Step Guide

Dlabs.ai

The Mayo Clinic sponsored the Mayo Clinic – STRIP AI competition focused on image classification of stroke blood clot origin. That’s why the clinic wants to harness the power of deep learning in a bid to help healthcare professionals in an automated way. The goal was to classify the blood clot origins in an ischemic stroke.

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Train and host a computer vision model for tampering detection on Amazon SageMaker: Part 2

AWS Machine Learning Blog

In the first part of this three-part series, we presented a solution that demonstrates how you can automate detecting document tampering and fraud at scale using AWS AI and machine learning (ML) services for a mortgage underwriting use case. The VGG-16 consists of 13 convolutional layers and three fully connected layers.