High-Performance Image and Video Annotation Tools and Quality ML Data At Your Fingertips.
One platform. For the entire team.
For the entire process.
Our platform streamlines the Computer Vision and Machine Learning development process via seamless data handling, smart annotation tools, and intuitive collaboration features. Our data visualization and cross-functional quality features identify data bias early, improve data accuracy, and enable faster, cost-efficient deployment of high-performance models.
Catch insights from your data early, even as data is being prepared. Our intuitive visualization tools allow statistical analysis on data at every stage. Seek out data imbalances early and iterate annotation specs more often without disrupting the process.
Tools that allow for time and cost savings, reducing the number of tedious tasks that need completed in order to annotate assets. Whether you are annotating an image or video, we have the tools necessary to create efficiencies and quality annotations.
Manage teams, timelines, and model development activities more effectively. Own end-to-end data annotation processes for better accuracy. Catch data imbalances, model biases, and performance shortfalls early to shorten development cycles.
“The Innotescus Annotation tool saved me a lot of time on a major delivery. I was able to use their video annotation to generate over 50,000 annotations in a short period of time. The management tools are wonderful and helped me keep my small team of annotators on track. And, I understand, new features are on the way. Online examples and support had me up and running in one day. Kudos to Innotescus for this exciting new entry into the annotation ecosystem. “
-Alex Terrazas PhD, Chief of AI and Cognitive Robotics at RE2 Robotics
“We assigned only one night to labeling data. If we didn’t have the Innotescus platform, we would not have been able to get the work done. Having the ability to leverage the Innotescus tools enabled us to focus on more critical aspects of the project.”
– Shasa Antao, MRSD Graduate Student, Carnegie Mellon University
“The Innotescus team was very responsive to our questions and requests. They have developed the product leveraging input from a variety of users, including those labeling the data as well as those using the data to ensure the best quality of data.”
– Shaun Lu, MRSD Graduate Student, Carnegie Mellon University
Ready to build better data? Let us show you how Innotescus can help.
We’re excited to show you what we’ve built. Get hands-on with our advanced annotation and dataset analytic tools.
We love this stuff.
The industry insights that fuel our desire to create the best-in-class machine learning annotation platform.
Synthetic Data: What, Why, and How?
Is There 20/20 Vision in Computer Vision?
In this post, we discuss how man and machine can work together in the image annotation process to make computer vision training more robust.
Computer Vision Projects Help Support the Fight Against COVID
In this post, Innotescus spotlights several computer vision/ML projects that support the fight against COVID. including a ML model that interprets chest x-rays.
A Complete Guide to an Iterative Process for Data Cleaning
In this blog, we discuss how you can improve the data cleaning process through iteration and prioritization. You can also download a FREE data cleaning rulebook template.
Writing an Effective Annotation Specification Document for Image Annotation
Let’s face it, most data scientists dread annotation. In this blog, we recommend key requirements for an annotation specification plan.
Data Labeling Tools to Get Your Next ML Project to Production
What are the odds that your ML project will reach production? 13% or a little better than one in ten. In this blog, we provide you with considerations to tilt the odds in your favor.
Reducing Data Bias – ML’s Great Challenge
Arguably, the most important challenge facing ML today involves data science and social equity. In this blog, we discuss how bias can be introduced into ML models, most often inadvertently.
Final Stages: Choosing, Training, and Deploying a Machine Learning Model
The last steps left – choose, train, and deploy a model. In this post, we discuss considerations when ML scientists finalize their solutions.
Blending Machine Learning and Domain Knowledge with Feature Engineering
Data augmentation is effective when addressing dataset shortcomings. In this blog, we’ll discuss augmentation basics, and techniques for augmenting image data.