Labelbox

Training data quality is critical for a machine learning model’s performance. Quality is measured by both the consistency and the accuracy of labeled data. The industry standard methods for calculating training data quality are benchmarks (aka gold standard), consensus, and review. As a data scientist in AI, an essential part of your job is figuring out what combination of these quality assurance procedures is right for your project.

How to Measure Quality when Training Machine Learning Models

In order for data science teams to outsource annotation to a managed workforce provider — also known as a Business Process Outsourcer (BPO) — they must first have the tools and infrastructure to store and manage their training data. Data management tools and infrastructure should support R&amp;D product management teams, outsourced labeling teams, and internal labeling and review teams working together in a single centralized place with fully transparent oversight.

How to Scale Training Data

Ready to learn how to train your AI in less than a day without writing a single line of code? If you are sitting on a pile of data that you want to make useful, but AI seems too daunting of a task to get started, then this article is for you. Or if you have experienced the trenches of AI and are ready to make the model training process a whole lot simpler and faster, you will also find this article eye-opening.

AI is More Accessible Than You Know

Is your model not performing well? Try digging into your data. Instead of getting marginal improvements in performance by searching for state-of-the-art models, drastically improve your model’s accuracy by improving the quality of your data.

It all Boils Down to the Training Data

September brought many new features and updates to Labelbox. But most notable is that the image annotation interface has undergone a massive upgrade.

Introducing best-in-class image annotation tools for computer vision applications

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The Critical Role of Data Annotation in Shaping the Future of Generative AI

I decided to try and recreate Windows 98 using nothing else apart from CSS and HTML.

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70-Page Report on the COCO Dataset and Object Detection [Part 1]

In this article, we’ll dive into the importance of data curation for computer vision, as well as review the top data curation tools on the market.

5 Best Data Curation Tools for Computer Vision in 2021

We are gradually encoding human knowledge in seas of annotated data

Artificial Intelligence: Multimillennial Data Transmitted To Machines With Brains

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How to Build Neural Network that Recognizes People Wearing Masks

In the rise of robotics, computer vision and image processing cameras, image annotation comes as the first step to get the right AI training data for Deep Learning models. Whether you build an app to allow users to snap fashion items at the store as a new omni-channel sales or use machine vision installed at edge device at the industrial facility to monitor anomalies: it starts with training massive image data sets.

Image Annotation Business Models [Reviewed]

CSS, or Cascading Style Sheets, might not be the first language you think of when making games for the web. Heck! It isn’t even a Turing complete
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styling language, no loops, if-else statements or any of those fancy 
things here.


How To Make a CSS Game Without Using JavaScript [Step-by-Step Guide]

We at <a href="https://humansintheloop.org/" target="_blank">Humans in the Loop</a> are constantly on the lookout for the best image annotation platform that offers multiple functionalities, project management tools and optimization of the annotation process (even 1 second less per image matters when you have to annotate 50k images!).

The best image annotation platforms for computer vision (+ an honest review of each)

Result: predictable costs, fewer incidents, reproducible jobs across environments.

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Labelbox is the simplest platform to train and operate machine intelligence

LABELBOX

labelbox.com

101-250 emps

Since 2018

Worth 1B

Labelbox's stories on HackerNoon

How to Measure Quality when Training Machine Learning Models

How to Scale Training Data

AI is More Accessible Than You Know

It all Boils Down to the Training Data

Introducing best-in-class image annotation tools for computer vision applications

Toward Holistic Evaluation of LLMs: Integrating Human Feedback with Traditional Metrics

The Critical Role of Data Annotation in Shaping the Future of Generative AI

Recreate Windows 98 With CSS

70-Page Report on the COCO Dataset and Object Detection [Part 3]

70-Page Report on the COCO Dataset and Object Detection [Part 1]

5 Best Data Curation Tools for Computer Vision in 2021

Artificial Intelligence: Multimillennial Data Transmitted To Machines With Brains

How to Build Neural Network that Recognizes People Wearing Masks

Image Annotation Business Models [Reviewed]

How To Make a CSS Game Without Using JavaScript [Step-by-Step Guide]

The best image annotation platforms for computer vision (+ an honest review of each)

Stop Hacking SQL: How to Build a Scalable Query Automation System

How to Build a File Uploader Tool with Drag-and-Drop and Cloud Storage

How AI Is Revolutionizing Agile Program Management with Confluence & Streamlit

How to Build a GUI-Based Currency Converter App in Python

Let's Build an MLOps Pipeline With Databricks and Spark - Part 1

How to Create Scrollable Lists with Protocol-Oriented Design & UICollectionViewCompositionalLayout

Analysis of Network Graphs: Visualizing Hamilton Characters as a Social Network

Breaking Down Data Silos: How Apache Doris Streamlines Customer Data Integration

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