Data annotation & labeling blog.

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Maxim Dupont

Maxim Dupont is the founder of LabelVisor, a scientist & software engineer. His hobby of writing tech reviews has become his new way of life. His belief is that together we can achieve the best world.

deepfake

This decade will be the decade of deepfake advertising

Marketing is becoming more and more manipulative. Artificial intelligence (AI) and machine learning are used to create convincing, true-to-life synthetic replicas that are nearly impossible to spot. Deepfake advertising is here. "Preparing for an Era of Deepfakes and AI-Generated Ads: A Framework for Understanding Responses to Manipulated Advertising"

Facial Recognition

Tight Bounding Boxes in Facial Recognition Technology

Accurate and precise object detection is crucial in facial recognition technology. One of the key components in achieving this accuracy is the use of tight bounding boxes. These bounding boxes play a vital role in identifying and localizing facial features, enabling the system to accurately classify and recognize individuals. A

Bounding Box Annotation

Automated vs. Manual Tight Bounding Box Annotation: A Comparison for Image Annotation Projects

Data annotation is a crucial step in machine learning and computer vision, and two methods have emerged: manual data annotation and augmented annotation. In this article, we will explore the pros and cons of each method and compare them to help you choose the best approach for your image annotation

Synthetic data

Synthetic data can enhance machine learning.

It is possible to teach a machine to recognize human actions in a variety of ways, including automatically detecting workers who fall on a construction site. A smart home robot could interpret a user's gestures using this technology. In order to accomplish this, researchers use a vast database

Bounding Boxes

Role of Tight Bounding Boxes in Enhancing Model Accuracy

Create an image showing how tight bounding boxes can help improve the accuracy of machine learning models used for object recognition. Incorporate examples of objects with and without tight bounding boxes to illustrate the difference in accuracy. Use contrasting colors or shading to highlight the difference in performance between models

Bounding Boxes

Comparison: Tight Bounding Boxes vs. Loose Bounding Boxes

Image annotation is an essential process in computer vision, AI, and machine learning. It involves labeling objects within an image to facilitate image recognition. One of the critical components of image annotation is bounding boxes. Bounding boxes define the boundaries of objects within an image, making it easier for the

machine learning

TOP hard and soft skills for data annotators

Data annotation is a critical component of machine learning, and it requires a unique set of skills to ensure accuracy and efficiency. As more companies invest in AI technologies, the demand for skilled data annotators is on the rise. However, it's not just about having technical expertise. The