Data annotation & labeling blog.

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Machine learning

Machine learning

Machine learning is successfully applied to a variety of disciplines, but if you're planning on entering the field there's one challenge that most data scientists face. How do you know what machine learning algorithms or techniques will work best for your application? This blog shares some tips and tricks for using data annotations for machine learning in different scenarios.

AI model

Mastering AI Model Lifecycle Management

Tasks like data preparation and preprocessing can take up to 80% of time in an AI project. This shows the importance of managing the AI model lifecycle well. Doing so ensures better results and maximizes AI investment value. AI model lifecycle management involves many phases. Each phase is key to

Object detection

YOLOv8: A Game-Changer for Video Surveillance

The security and technology worlds have come together in a big way. This union has spurred innovations we could barely imagine a decade ago. Now, YOLOv8 technology is bringing a major change to how we monitor areas in real time. It's the eighth version of an object detection

Object detection

Boosting Accuracy in Object Detection: Tips for YOLOv8

YOLOv8 stands out in the realm of object detection for its superior real-time performance and accuracy. This model uses deep learning to swiftly spot objects in images. It's a vital asset for sectors such as self-driving cars, security systems, and software that recognizes images. For the best results

Data Labeling

Choosing the Right Outsourcing Partner for Data Labeling.

Data labeling plays a crucial role in AI and machine learning projects, as it involves annotating and tagging data to train and improve algorithms. However, data labeling can be a time-consuming and resource-intensive task for businesses. That's where outsourcing data labeling services comes into play. By partnering with

computer vision

Enhancing Team Collaboration with CVAT

In today's fast-paced and ever-evolving world of AI annotation, collaborative teamwork is crucial for success. CVAT (Computer Vision Annotation Tool) is a cutting-edge collaborative annotation platform that empowers teams to achieve seamless collaboration and efficiency in their AI-powered computer vision projects. With its comprehensive suite of features, CVAT

traffic signal

Traffic signal control algorithms are developed using meta-reinforcement learning

In urban areas, traffic signal control has a significant impact on day-to-day life. Based on the traffic conditions, the existing system utilizes a theory-based controller to alter the traffic lights. During unsaturated traffic conditions, the objective is to minimize vehicle delays while maximizing vehicle throughput during congestion. Nevertheless, the existing

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