AI is transforming healthcare by arming practitioners with more information at the right times to make better decisions and fewer errors. Here’s how image annotation makes it ...
Images and videos are both means to an end to annotate visual data. Each may have its own unique process but in the end individual frames are being annotated on a meta data level. ...
Image annotation holds great promise for these centuries-old industries that rely heavily on visual data and are ripe for AI innovation.
CloudFactory and V7 Labs annotated chest x-rays and trained ML models to identify issues including COVID-19. The dataset is now available for research.
Some data annotation tools won't be a good fit for your AI and machine learning project. Keep these six important features in mind as you evaluate tool providers.
Even in uncertain times, you’re swimming in an ocean of data. If you’re using AI, how you process and use that data will determine the future of your business.
Data scientists at Hivemind created 3 data labeling tasks and hired 2 teams to complete them. The differences in data accuracy, speed, and cost may surprise you.
Your in-house data scientists shouldn't be doing tedious data labeling work for machine learning projects. They should be focusing on more important innovation.
Not all outsourced data labeling partners are a good fit for every AI project. Here are 5 things you need to consider before, during, and after vendor evaluations.
It is not enough for workforce vendors to tout a history of quality and speed. They must prove they can serve your needs during and after COVID-19.
No matter how robust your initial training may be, keeping your machine learning models up-to-date is essential. Here are two retraining approaches.