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What is The Difference Between Data Annotation and Labeling in AI & ML?

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Though, Data labeling and annotation are the words used interchangeably to represent the an art of tagging or label the contents available in the various formats. Nowadays both of these techniques are basically used to make the object or text of interest recognizable to machines through computer vision. Data labeling is the process of tagging the data like text or objects in videos and images to make it detectable and recognizable to computer vision to train the AI models through machines learning algorithm for right predictions. Labeling basically done with useful tags or added metadata to make the texts more meaningful and informative making it understandable to machines. And usually texts and images are labeled but nowadays annotation is also used for the same purpose and labeling is done for machine learning training.


What Are The Applications of Image Annotation in Machine Learning and AI?

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At the time of developing the AI models through machine learning (ML) first and most important thing you need, relevant training data sets, which can only help the algorithms understand the scenario through new data or seeing the objects and predict when used in real-life making various tasks autonomous. In the visual perception based AI model, you need images, containing the objects that we see in our real life. And to make the object of interest recognizable to such models the images need to be annotated with the right techniques. And image annotation is the process, used to create such annotated images. The applications of image annotation in machine learning and AI is substantial in terms of model success.


What is the right Data Annotation Process for Training the Machine Learning Algorithms?

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Data annotation in AI world is one of the most crucial processes to make available the set of training data for machine learning algorithms. And computer vision based AI model needs annotated images to make the various objects recognizable for better understanding of surroundings. Data annotation process involves from collection of data to labeling, quality check and validation that makes the raw data usable for machine learning training. For supervised machine learning projects, without labeled data, it is not possible to train the AI model. During the whole process, well trained human power with right tools and techniques, data is annotated as per the requirements and then processed in a highly secured environment to clients.


Learning Spiral in Computer Vision – Blogs

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Computer vision is a field of study that seeks to develop techniques to help computers see and understand the content of digital images such as photographs and videos. Our work in Computer Vision & Machine Learning powers innovation in areas of various sectors through Accurate & high quality labeled Data from our Professional & well-trained annotators. Computer vision technology is very highly significant and dynamic and it's been selected by many industries in many different ways. The difference is some use cases happen behind the more visible or some are not. Computer vision helps the automotive industry in many ways it offers a platform and We generate accurate and diverse annotations on the datasets to train, validate, and test algorithms related to autonomous vehicles.


How to Annotate Images for Deep Learning?

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Deep learning is a subset of machine learning (ML) which is a sub discipline of artificial intelligence (AI). Deep learning is used to carry out more crucial tasks without being explicitly programmed to do so. Actually, in deep learning neural networks are used to analyze data and extract relevant patterns of information from them. And the neural networks are divided into three different mechanisms an input layer, a hidden layer, and an output layer. And when many small networks are joined together into layers, a deep neural network is created.