Blog

Using model-assisted labeling to speed up annotation efficiency with Labelbox

Recently, a team of researchers at the Institute of Industrial Science, a part of The University of Tokyo, leveraged Labelbox's model-assisted labeling features in order to speed up their machine learning processes by 2-3x.

Asking good questions isn’t the first step to building enterprise ML, it’s step 0

When email first came on the scene, it was revolutionary. People were excited to try a faster method of communicating with friends and business contacts. But of course, someone quickly found a way to ruin it. Spam (unwanted bulk emails, not the canned meat product) started popping up in huge numbers, slowing down network speeds and people’s productivity. If we wanted to improve internet speeds, we’d have to identify spam before it clogged up inboxes. Enter machine learning. Trained machine lea

Managing a Labeling Team and Building a High-Impact Deep Learning Model for Tree Identification

I participated in an amazing AI challenge through Omdena’s community where we built a classification model for trees to prevent fires and save lives using satellite imagery. Omdena brings together AI enthusiasts from around the world to address real-world challenges through AI models.

Introducing Image Segmentation at Labelbox

We set out to create a tool that makes image segmentation fast and accurate to make it accessible to more computer vision teams and projects.

Labelbox Adapts to Support American Family Insurance Automation

In this article, we discuss why and how we built a new labeling ontology feature to support American Family's use case. Labeling ontology is critical for machine learning applications. It determines what the labeler can label and, in turn, the categories the model will be able to identify.

Model Predictions, Semi-Automatic Labeling and Quality Assurance in Production

Model predictions play two vital roles in a machine learning pipeline. Predictions can be used to accelerate labeling speed as well as test and improve the accuracy of production models.

How to Measure Quality when Training Machine Learning Models

With quality assurance processes data scientists can monitor overall consistency and accuracy of training data, quickly troubleshoot quality errors, improve instructions, on-boarding, and training of labelers, and better understand the project specifics on what and how to label.

The Internet of Cows: AI in Agriculture with SomaDetect

SomaDetect SomaDetect provides dairy farmers with the information they need to produce the highest quality milk with the resources they have today, for a more sustainable dairy food system. An important part of making this happen is is machine learning and computer vision. We spoke with Bharath Sudarsan, Director of AI, who went into a little more detail about how SomaDetect is making a splash through the internet of cows. “Our basic business goal is to make better milk. We have IoT devices t

Lytx: Using Data to Help Save Lives on Our Roadways

Lytx® — as in analytics — harnesses the power of video to transform transportation fleets with improved safety, efficiency, productivity and profitability. Lytx pioneered the video telematics category two decades ago when it launched its DriveCam® safety program, and remains the market leader today. With more than 500,000 subscriptions, Lytx protects over 3,000 commercial and government fleet clients and 850,000 drivers. The company has analyzed more than 80 billion miles of driving data — addin

Genius: Artificial Intelligence Transformation of Pro Sports

Genius Sports is a UK-based sports data company who is leveraging computer vision and deep learning to lead the digital transformation of sports intelligence, working on an international scale with top sports organizations such as MLB, PGA Tour, and the Premier League. This May, the NCAA announced its 10-year partnership with Genius, the goal of which is to develop and implement new data capture and analysis services, coaching insights, and real-time statistics that can be used to improve gamep