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.

machine learning

Future Trends and Developments in YOLO

As we look ahead, the future of YOLO promises even more impressive advancements in real-time object detection and computer vision applications. YOLO Vision 2024, an upcoming hybrid event scheduled for September, 2024, at Google for Startups Campus in Madrid, will showcase the latest YOLO features and object detection future. This

Data annotation

Using Label Studio for Autonomous Driving Video Annotation

Label Studio, a leading open-source data labeling tool, is favored by over 250,000 machine learning experts and data scientists globally. It has a significant role in transforming autonomous driving video annotation. Label Studio provides a comprehensive solution for annotating diverse data types, including video essential for self-driving vehicles. Its

Data annotation

Solutions and Workarounds for YOLOv10 Challenges

YOLOv10 brings impressive advancements to the table. With models ranging from Nano to Extra Large, it caters to diverse computational needs. The Nano version, with just 2.3 million parameters, achieves a remarkable 38.5% APval at a swift 1.84ms latency. On the other end, the X version boasts

Machine learning

Label Studio in Healthcare: Medical Image Annotation

Label Studio, with 18.2k stars on GitHub, is a top choice for medical image labeling. Its open-source nature and customizable features make it perfect for healthcare professionals looking for efficient ways to label medical datasets. The platform's flexibility allows for seamless integration with machine learning models. This

Data annotation

Common Issues in YOLOv10 Implementation

YOLOv10, the latest YOLO object detection framework, eliminates non-maximum suppression (NMS)? This could reduce latency by up to 30%. This breakthrough in real-time object detection has garnered significant interest from developers and researchers. Developed by Tsinghua University researchers, YOLOv10 introduces a new approach to object detection challenges. It uses Consistent

Data annotation

Avoiding Common Pitfalls in Data Annotation

A staggering 80% of time spent on AI projects is dedicated to data management tasks. As the global data labeling market races towards a projected $13 billion value by 2030, the stakes for avoiding data annotation mistakes have never been higher. In the rapidly evolving field of artificial intelligence, annotation

machine learning

Flexibility and Scalability of YOLOv10

YOLOv10 can process images at an astonishing rate of up to 1000 frames per second. This breakthrough in real-time object detection marks a quantum leap in computer vision. As we explore YOLOv10's flexibility and scalability, you'll see how it's reshaping AI-driven perception. YOLOv10, the