2 Ways to Harness Pre-Annotation for Machine Learning
For every new application of Computer Vision-based Machine Learning, a newly annotated training dataset is needed to train a model; as the rapid proliferation of
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
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.