Latest updates on Segments.ai and data labeling
10 Lidar Datasets for Autonomous Driving
Lidar sensors use laser beams to capture the world in 3D. The sensors output 3D point clouds, which are simply collections of points in 3D. Machine learning models can be used to detect and track objects in these point clouds, or even to classify every single point (segmentation). This enables autonomous vehicles to [...]
ISO 27001 certification, improved docs, and more
Big news: Segments.ai got ISO 27001 certified! ISO 27001 is the international standard for information security, with the goal of protecting all data within an organization. What this means for you is that your data is in safe hands with us. Improved docs We've been working hard on improving our documentation. You'll notice [...]
Introducing Text Labeling
This blog is about a part of the Segments platform that is no longer available today. If you have any questions about this, please contact hello@segments.ai. Segments.ai has great labeling tools for computer vision. As multimodal learning is becoming increasingly important, even computer vision teams sometimes need to label other data like text or [...]
Introducing Autosegment
Segments.ai has always been a great tool for creating image segmentation labels; it’s in the name! Our superpixel technology allows you to segment an image in just a few clicks. However, superpixels aren’t well-suited for segmenting fine/small objects, or for segmenting very high-resolution images. That’s why we’re happy to announce a new assistive [...]
Panoptic Segmentation: Introduction and Datasets
In this article, we’ll look at what panoptic segmentation is, which public datasets exist, and how you can create your own panoptic segmentation dataset. What is panoptic segmentation? Panoptic segmentation is the combination of instance segmentation and semantic segmentation. It was first introduced in a 2018 paper by Kirillov et al. [...]
Create a Data-Centric ML Pipeline with W&B
Most machine learning researchers focus on improving models and creating new kinds of models. However, when you’re building an ML system for real-world use, the data you use for training your model is often more important than the specifics of the model itself. That’s why a new paradigm has emerged within the ML [...]
