One Solution. Many Uses.
Innotescus’ scalable platform provides fast and accurate annotation tools, meaningful analytics, and transparent collaboration and management features. Give your machine learning tools a fighting chance by using an interface built with simplicity and scalability in mind.
User-centered design has produced features that satisfy every member of the ML development process – from Computer Vision engineers to project managers and annotators.
Image Annotation
The specialized tech stack allows for particularly fast, yet powerful, tool sets that accommodate everything from basic image classification to highly accurate pixel-by-pixel segmentations.
Assisted Annotation
Take your annotations to the next level. Assisted annotation tools improve the speed and accuracy of your annotators.
Video Annotation
Pixel-perfect Semantic Segmentation coupled with AI-Assisted annotation tools requires fewer frames to be annotated manually. The proprietary tracking algorithms enable thousands of frames of video to be annotated with a single click.
Data Analytics
Quickly understand the characteristics of your dataset and annotations. Find unbalanced classes to determine where datasets need to be augmented for improved model performance.
Annotation Operations
Collaboration tools for team management, communication, annotation review, and automated annotation consensus. Get actionable quality and efficiency metrics on annotation team performance including Innotescus’ proprietary consensus algorithm.
I'm ready to build better data.
Still have questions?
What kind of annotation does Innotescus provide?
The Innotescus Annotation Platform can accommodate classification, object detection, instance segmentation, and semantic segmentation annotations on images. Also coming soon is video annotation that combines pixel-perfect semantic segmentation with AI-assisted annotation tools that require fewer frames to be manually annotated.
What are the system requirements to use Innotescus?
Innotescus is a browser-based tool, so the requirements are minimal. Our platform has been tested on Google Chrome and Mozilla Firefox, operating on Mac, Windows, and Linux systems.
How safe is my data?
At Innotescus we know your data is your competitive advantage, so its security is our highest priority. Your data is encrypted at rest and in transit, and aside from the default layer of encryption in our storage services, each customer’s data is encrypted with a unique key, so only you can access and decrypt your data.
Does Innotescus provide third party annotators?
Innotescus does not currently provide an annotation workforce. Our platform does enable teams to work with third-party annotators, however, by providing project supervisors the ability to control each project member’s access level, and a special interface for members who only need to annotate, and don’t need access to the project data.
We love this stuff.
The industry insights that fuel our desire to create the best-in-class machine learning annotation platform.
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.
Using Computer Vision to Tackle COVID before It Spreads
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.
It’s the Journey – 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.
How To Write An Effective Annotation Specification Document for Machine Learning
Let’s face it, most data scientists dread annotation. In this blog, we recommend key requirements for an annotation specification plan.
Will Your ML Project Ever Make it 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 Model 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.
The 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.
Stretching your Dataset with Data Augmentation
Data augmentation is an effective way to address the shortcomings of a dataset. In this blog, we’ll discuss the basics of augmentation, and the common techniques for augmenting image data.