Synthetic Computer Vision aims to translate what’s in the Virtual world back to the Real world. (Image by author)

🗣️ The Current State of Computer Vision

As of today, there has been over…

Improving model generalisability with virtual synthetic data; A case-study for product recognition in retail

Last month at the Tesla AI Day, the world was stunned once again by Elon Musk’s ambitious goals to create a humanoid Tesla Bot designed to help us humans with boring and repetitive tasks. What some of you might have also seen was that exactly half an hour beforehand, the…

Moving closer to the real world

Introduction

In one of our previous blog posts and webinars, we’ve written about the power of synthetic data and Neurolabs’ synthetic data engine that transforms 3D assets into robust synthetic datasets for computer vision. At Neurolabs, we believe synthetic data holds the key for making computer vision and object detection more…

Reduce the domain gap with domain randomization

In this post, we’ll explore how we can improve the accuracy of object detection models that have been trained solely on synthetic data.

Why machine learning? Why simulate data?

Since the resurgence of deep learning for computer vision through AlexNet in 2012, we have seen improvement after improvement — deeper networks, new architectures, more availability in…

Oh no, another COVID-19 post. Well hopefully not, but I admit that enforced confinement to my house, and my experience of trying to shop has prompted this post.

Anyone who has tried to buy food in the last month will have experienced an empty shelf or two. In fact one…

Neurolabs

Using the power of synthetic data to democratise Computer Vision.

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