Improve Model Performance with Dataset 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.
Alleviate trial-and-error processes during model design by understanding data more thoroughly with exploratory data analysis (EDA).
Eliminate data distribution biases, increase the diversity of data, and construct training datasets with sufficient complexity for a variety of applications.
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The industry insights that fuel our desire to create the best-in-class machine learning annotation platform.
The 3 Major Advantages of Annotating Video
Almost all computer vision applications rely on annotated images to train, test, and validate the models that power them. Annotating these images can range in complexity…
The Next Big Thing in Annotation: Assisted Video Annotation
Machine learning is transforming businesses around the world. It’s already a $7.3 billion market and is expected to continue its explosive growth to $30.6 billion by 2024.
Synthetic Data: What, Why, and How?
It’s no secret that a comprehensive, well-labeled dataset goes a long way towards an effective Machine Learning solution, and while data collection is a large part of…
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…
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…