Named as a G2 2023 Winter High Performer and "Easiest To Use" in Data Labeling Software
Labellerr's export feature lets you export files in various formats like JSON, CSV, and YOLOv5 etc.You can choose the format that suits your needs – JSON for interoperability, CSV for easy data manipulation, or YOLOv5 for specific models.
In Labellerr, you also have the option to display a chosen set of files in Grid view for a speedy visual assessment and an easy review process. Additionally, the feature allows you to zoom in and out for a closer examination of each file.
In Labellerr, we've introduced a handy feature that lets clients or reviewers efficiently assess specific files i.e a subset of files. Whether it's evaluating files from a specific date or those linked to particular annotators, the chosen files are easily accessible and seamlessly transitioned to the review window.
With Labellerr's transparency feature, you can now adjust the transparency of overlapped objects while annotating in a file. This means you get to see each and every object clearly, avoiding any confusion caused by overlap, helping you focus on specific objects you want to see.
The Labellerr team is always working on introducing new features, and is making it possible for customers to build their own. In many objects, having additional human-judged metadata alongside each annotation for a specific task is beneficial.
In scenarios where datasets are extensive and categorization is crucial, Labellerr presents a solution through color-coded annotations. Labellerr's color-coded categorization not only simplifies the task of sorting through large datasets but also offers a visual representation that enhances understanding and efficiency.
Responding to their request, we've updated the error rate calculation on Labellerr. Rather than considering the total number of files, it now focuses on mistakes within a file, comparing them to the total number of objects. This adjustment ensures customers get a clear and accurate view of the real error rate based solely on the number of mistakes.
This video demonstrates how to filter files based on suggestions or mistakes in the remarks section. Examine remarks for valuable feedback and communication insights. Efficiently identify mistakes by using the tag filter on the files page. Dive deeper into each rejected file by opening its specific link.
Labellerr is all about making your annotation journey smoother, more efficient, and tailored to your needs. "Remarks Tags" is our way of improving communication based on your requests for a more detailed approach.
Ever wondered how to keep a close eye on your team's progress in data annotation? Are you facing challenges in efficiently monitoring the activities and velocity of each team member, across different file statuses?Introducing the Labellerr's Dynamic Dashboard!
How to effortlessly keep tabs on the real-time progress of your data annotation project? Introducing our Dashboard! Are you facing the challenge of tracking user activity and the status of your files efficiently? Say goodbye to manual tracking and hello to seamless annotation progress monitoring.
Tensor Matics Inc
651 N Broad St, Suite 201,
Middletown, New Castle 19709 Delaware
Tensor Matics Pvt Ltd
SCO 301-302, 35B, Sector 35, Chandigarh, 160035
Phone: +917565883102
