Computer vision.

Simply. For ALL

Using the power of synthetic data to democratise Computer Vision.

Why isn't everyone using Computer Vision to recognise objects? 

The reason is, largely due to the vast amount of data required to train Deep Learning algorithms to accurately recognise objects. In traditional Computer Vision approaches, acquiring and annotating that data is a manual process that is expensive and slow.​

Traditional methods are too expensive

McKinsey report listed data labelling as the biggest obstacle to Computer Vision adoption in the industry. 

Expensive, bad quality data sets

  • Low image variety

  • Missing a diverse distribution of examples

  • Biases in training data sets

Disappointing Outcomes

  • Low accuracy

  • Long time-to-deployment 

  • Lack of model robustness

  • Poor model 

  • Biases in model performance

Neurolabs is democratising 

Computer Vision

"A Computer Vision powered technology propelled by a synthetic data generation engine. Breaking down the adoption barriers and enabling rapid ramp-up of object recognition solutions across multiple use cases and industries."

 

 synthetic data GENERATION using 3D models in Virtual Environments

State-of-the-art model performance

Robust and accurate object recognition models. Successfully deal with extreme situations: partial occlusions, substantial background clutter, change in a real-world environment (e.g. light).

Photo-realistic simulation of complex real-life scenarios

Unlimited, custom-tailored training data with pixel-level annotations out-of-the-box. 

Synthetic datasets on-the-fly

50,000 labelled images created in 2 hours at only €15, compared to 45 days and €2,500 using traditional methods.

 
1/3
Optimal training datasets

A continuous feedback loop between data generation and model training ensures the best datasets are created for optimal performance and enable model interpretability and explainability.

Additional data modalities

Enhance model performance using additional & freely available training data from the synthetic engine: depth maps, point clouds or 3D volumetric data, usually unavailable in traditional manually-labelled real-world data. 

Diverse synthetic datasets  

Scene randomisation in the synthetic engine provides training images with the diversity needed to cover extreme and rare real-world scenarios. 

Neurolabs Computer Vision in Production

 
Object Recognition

We empower both small and large customers to implement state-of-the-art CV applications with minimal incremental resources and costs — no coding required, no tedious data labelling, no image gathering. 

We help our partners automate generic object recognition tasks in industries ranging from hospitality to retail and manufacturing.

 

Below are just a few of the things you can detect with Neurolabs.

Checkout lines in Hospitality

Localisation and control in Robotics

Utensils inventory in Healthcare

Quality control in Manufacturing

Crop grading and sorting in Agriculture

Recycling sorting

No coding
No image gathering
No data labelling
State-of-the-art accuracy
EASY TO INTEGRATE
MINIMAL CLIENT DATA
 

Contact

Bayes Centre

47 Potterrow, Edinburgh

Scotland EH8 9BT, United Kingdom

 

Silicon Forest

9 Cotita, Cluj-Napoca

Cluj 400104, Romania

​​

 

hello@neurolabs.eu

© 2020 Neurolaboratories LTD -

This site was designed with the
.com
website builder. Create your website today.
Start Now