The What and Why of Annotation Augmentation: Three Key Benefits
Everybody knows dataset bias exists. What’s more, most folks are very adept at identifying it. But once your bias is defined, how do you act on it? For Innotescus users, the
Curating Data for Transfer Learning with NVIDIA’s TAO Toolkit and Innotescus
This blog walks through using NVIDIA’s TAO Toolkit and Innotescus’ data curation and analysis platform to improve a popular object detection model’s performance on the ‘person’
How We Placed Second in Andrew Ng’s Data-Centric AI Challenge
Shashank, Chris, and Rob first worked together at ChemImage Corporation, leveraging machine learning with dense hyperspectral imaging datasets to identify chemical
Mapping Out the Metaverse with Machine Learning
After announcing plans to make Facebook a ‘metaverse company,’ the internet is abuzz trying to figure out what that even means. Plenty of folks have made half-decent
Learning Curve Graphs Part 2: The Experiment
In part 1, we discussed the data requirement curse for non-linear machine learning models. We saw that we can use learning curve graphs to estimate the dataset size for target
Learning Curve Graphs Part 1: Countering the Data Requirement Curse
Broadly speaking, most machine learning algorithms fall into one of two categories: linear models or non-linear models. Linear models are easy to interpret, faster to train and deploy
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.