Deepomatic or Matroid: Which one to choose ?

Matroid localizer gallery.

For this article, and for all from this series  we have chosen the 6 market leaders and to evaluate them, we set up the same three different projects on each platform. This gave us a sense of the features each platform provides and the associated shortcomings if any. Then we computed the model performance which gave us a good insight into the viability of using the platform for production-level business applications. If you want to read in detail our methodology, please click here.

Matroid is part of those leaders but took on a hybrid approach very early on. Instead of providing pretrained models on one side and custom models on the other, they tried to find the best of both worlds.

Practically it means they built a huge collection of concepts with the idea that you could quickly pick them out to build the custom model that you need. This showed during the experimentation when we could not upload the pre-annotated data for all our projects, thus not being able to compare the models performances.

This approach leads to the same shortcomings as all of-the-shelf models. When it comes to enterprise applications: it just doesn’t cut it. It works for a quick demonstrator, but if you want to put it in production you need to be able to tailor it to your own use case, which always has its specific attributes. Although the promise was very appealing, today it seems they have not put out any large scale enterprise application.

At the same time, Matroid also invested heavily in their video stream integration, being able to, for instance, integrate with television streams such as Bloomberg and automatically monitor basic elements or search for similar looking images.

Matroid has a strong presence in the bay area, as they organize the ScaledML conference, but we could not even set up the sample projects on their platform. Data input was severely limited and their detector capabilities are still nascent.

And now, Deepomatic

Deepomatic project overview page.

Deepomatic is at the other end of the spectrum. Here, the value proposition is to enable the largest possible audience to create and deploy Enterprise computer vision applications. This means providing customers with a one-stop platform where everything is integrated, making it as easy to use as possible while promoting industry best practices.

Practically Deepomatic provides an easy-to-use annotation interface deeply linked to the model training. This means models are used to speed up annotations with active learning, but also to review existing annotations with error spotting, this alone can reduce annotation errors by up to 10% according to our latest tests.

Training is performed seamlessly with a few clicks and a full-featured performance dashboard is then used to analyze the model and identify potential improvements.

Unfortunately, training a model is not the end of story when it comes to Enterprise applications. You then need to be able to package your model, chain them to form complex applications, version and monitor them while being able to deploy them either on public cloud, on premises or at the edge. All of which are built-in capabilities of the Deepomatic platform.

Only then you can focus on closing the loop, automatically sending interesting images back to the platform to improve model performance in a virtuous circle. Finally, Deepomatic provides a built-in monitoring dashboard to follow day-to-day field operations and an analytics dashboard to perform BI analysis on long-term business trends.

Deepomatic is the go-to-platform if you want to be able to address your whole enterprise applications lifecycle from a centralized place with built-in industry best practices and state of the art models. This is the most feature-rich platform while at the same time requiring the least amount of coding and development skills.