Tag: artificial intelligence
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Dataset Development Lifecycle
Google has come up with a framework for data collection inspired by software development concepts in a 5-step cyclical process. In this article, we will examine google’s proposed data collection framework.
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Attention Mechanism: from NLP to Computer Vision
In this article, we discuss the Attention mechanism and trace its history of use from natural language processing to computer vision. And finally, we will examine transformers and their application in computer vision.
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Classify Broken and Normal Bones in X-ray Images using deep learning
Nowadays with the help of computer vision technology and image processing we can classify broken and normal bone X-ray images with high accuracy. In this article, we will discuss the Deep Learning approach for this purpose.
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Annotating Computer Vision Projects
Choosing the right images for training, validating, and testing computer vision algorithms will significantly affect your AI project’s success. To train an AI model for object detection, segmentation, and classification with human-like performance, each image in the dataset must be labeled thoughtfully and accurately. This article examines dataset annotation and labeling techniques.
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What Is Data Augmentation ?
To train a model or use transfer learning in machine vision, there must be enough data. Data Augmentation is a very important step that helps us increase our training data. The purpose of this article is to examine this feature and to review the techniques used to increase data on the AIEX platform.
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Train, Test, and Validation Datasets
An artificial intelligence model output is affected by how we divide the input dataset. There are several factors to consider when choosing a data partitioning method. In this article, we will examine the types and uses of these divisions.
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Computer Vision in the Metaverse
Metaverse is one of the fastest-growing technologies today. A comprehensive review of computer vision concepts in Metaverse is presented in this study.
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Model-Driven Vs Data-Driven Approach
An AI model’s performance can be increased by either improving the dataset or the model’s structure. The purpose of this article is to examine these two approaches and determine which one is the most efficient.
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How Tensorboard Works
In this article, we will introduce Tensorboard and explain how it can be used on AIEX.
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AI comes into Art
The use of Artificial Intelligence is spreading to all fields. In this article, we will focus on the practical applications of AI in the arts.