And which approaches you can choose from for your computer vision projects

By Greta Elvestuen, PhD, Data Scientist consultant at Inmeta

Most humans are able to naturally navigate through many social interaction scenarios, having an intrinsic capacity to reason about other people’s intents, beliefs and desires. They use such reasoning in order to predict what might happen in the future and make corresponding decisions. As technology develops, the relevance of this ability to predict changes in an environment, as well as object behavior, has been constantly increasing throughout the recent decades.

Hence, motion prediction has found its way into a…

By Vadim Frolov, Data Scientist at Inmeta

Neural networks (NN) and computer vision models in particular are known to perform well in specific tasks, but often fail to generalize to tasks they have not been trained on. A model that performs well on a food data may perform poorly on satellite images.

A new model from OpenAI named CLIP claims to close this gap by a large margin. The paper Open AI wrote presenting CLIP demonstrates how the model may be used on a various classification datasets in a zero-shot manner.

In this article, I will explain the key ideas…

By Anna Rabstad, Service and UX designer at Inmeta

When you have done your research, how do you make sense of it? You probably want to extract some insights from the research, but how? This is a short introduction to what insights is, and how to discover them.

This article was born after I did a short talk on this topic at Pecha Kucha Night in Hamar for a limited audience and was afterwards challenged to embroider it further. Why not? — was my thought and here we go.

Welcome to a short introduction to insights, what it is in…

By Vadim Frolov, Data Scientist at Inmeta

Demo web page

With ML projects still on the rise we are yet to see integrated solutions in almost every device around us. The need for processing power, memory and experimentation has led to machine learning and DL frameworks targeting desktop computers first. However once trained, a model may be executed in a more constrained environment on a smartphone or on an IoT device. A particularly interesting environment to run the model on is browser. Browser-based solutions may be used on a wide range of devices, desktop and mobile, online and offline. …

And why would you want to apply them in your machine learning projects.

By Greta Elvestuen, PhD, Data Scientist consultant at Inmeta

Throughout the last decade, convolutional neural networks (CNNs) have brought significant improvements in performance to machine learning models. Especially deep learning models have gained high popularity within the field of computer vision, due to their vast achievements and implementation in a wide variety of everyday applications.

However, training such networks is very time-consuming. Large datasets are necessary in order to train a high performing model, leading to excessive training times for up to several weeks. This is especially…


True innovation lies at the crossroads between desirability, viability and feasibility. And having fun while doing it! →

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