What is the use of Image Annotations: How To Label Image Dataset?

Annotation and labeling of Images is highly in demand owing to growth in AI and machine learning (ML) developments. The annotation and labeling helps the models developed on AI or ML algorithms to learn from set of data and use for future prediction.

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How to Decide Important Points While Outsourcing Image Annotation?

Things To Consider While Outsourcing The Image Annotation Services

Outsourcing image annotation services is one the most crucial task for AI companies seek training to develop the models. Actually, machine learning training data is a kind of fuel works for algorithms to learn from various patterns and predict in the same way.
And you need to be very careful while outsourcing your project to such companies to get the best quality data at least cost. So, in context of the same we brought here the key points to be considered while outsourcing the image annotation services.

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What is Data Annotation and What are its Advantages?

AI and machine learning is one the fastest growing technology brining unbelievable innovations providing the advantages to different fields globally. And to create such automated applications or machines, huge amount of training data sets is required.

And to create such data sets, image annotation technique is used to make the objects recognizable to computer vision for machine learning. And this annotation process is benefiting not only the AI filed but also providing advantages to other stakeholders. Here we will discuss about the advantages of data annotation in various fields.

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Semantic Segmentation vs Object Detection – Difference

Image annotation is playing a crucial role in object recognition for computer vision based machine learning algorithm training. There is huge difference between them, as one of them is done for another purpose and we will discuss about both of them.

What is Object Detection in Machine Learning or AI?

The term “object detection” apparently illustrates, any kind of object need to be detected for different types of perception needs. For machines, computer vision based technology is used to recognize the physical objects in an image or video and when such huge amount of labeled or annotated images are used to train an algorithm, a perception model helps to develop the AI based model for predictions in future.

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How Bounding Box and Tagging Can Improve Online Shopping Experience?

Computer vision based technology helps machines to recognize the different types of objects without human interference. Actually, using the right tagging of products through certain technique is important to improve the customer’s shopping experience.

Bounding Box is the most popular image annotation technique suitable for ecommerce and retail industry brining a more interactive experience for the customer’s while searching a particular product online.

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What is the Role of Image Annotators in Machine Learning and AI?

Image annotators are playing a key role in machine learning (ML) and AI development. Actually, in machine learning, certain data sets are used to train the algorithm, that learns the patterns and make the productions in real-life use as per the data used.

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What is The Difference Between 2D and 3D Image Annotations: Use Cases

Image annotation is the only technique helps machine learning or AI-based perception model to recognize the particular object in an image and learn to detect the such objects when used in real-life. And there are different types of image annotations, like polygon, polylines, landmark, semantic segmentation and bounding box which is one the most common image annotation technique used to make the objects recognizable into 2D and 3D formats.

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