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.

Data annotation

Advanced Use Cases of YOLOv10

The architecture of YOLOv10 is meticulously designed to balance precision with computational efficiency. It incorporates innovative features like large-kernel convolutions and partial self-attention modules. These elements boost its performance without compromising on efficiency. This makes YOLOv10 a top choice for AI-powered surveillance and real-time tracking. YOLOv10's versatility is

computer vision

Collaboration Features in Label Studio

Label Studio stands out as a leading platform for team collaboration in data annotation projects. It simplifies your machine learning workflows with its advanced Collaboration Label Studio features. The tool supports a wide range of data types and offers templates for rapid project setup. This makes it perfect for teams

computer vision

Real-Time Object Detection with YOLOv10

YOLOv10 represents a groundbreaking advancement in computer vision technology. It significantly enhances real-time object detection capabilities. By leveraging advanced neural networks and deep learning, YOLOv10 achieves unmatched speed and precision. Accessible on GitHub, YOLOv10 garnered 8.8k stars and 780 forks at the time of writing. It's licensed

computer vision

Automation and Scripting in Label Studio

In the realm of machine learning, data preparation is paramount. Label Studio brings forth automation features to simplify your machine learning data labeling endeavors. This open-source tool accommodates a variety of data formats, from text to video, and harmonizes effortlessly with ML projects. Label Studio's automation boosts efficiency

Data annotation

Applications of YOLOv10 in Object Detection

YOLOv10 introduces several groundbreaking features. Its NMS-free training strategy and consistent dual assignments streamline the inference process. These innovations lead to a reduction in latency, facilitating quicker real-time object detection. The model incorporates large-kernel convolutions and partial self-attention modules, boosting performance while keeping computational demands manageable. YOLOv10's advancements

Data annotation

Creating and Using Custom Annotation Templates in Label Studio

During the setup of your Label Studio project, you'll discover numerous customization options for the labeling interface. These tools enable you to design templates that align with your data structure and annotation requirements. Custom templates in Label Studio can notably boost your labeling efficiency, particularly for complex datasets

computer vision

Best Tools and Frameworks for YOLOv10

YOLOv10, the latest in the YOLO series, introduces significant advancements in object detection. This guide delves into the top tools and frameworks for leveraging YOLOv10 in deep learning projects. We'll explore essential resources, from PyTorch to TensorFlow, to aid in harnessing this advanced technology. YOLOv10 brings forth key