Deepomatic or Clarifai: Which one to choose ?

Clarifai model performance page.

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.

Clarifai is part of those leaders and was the very first company to focus exclusively on deep learning in computer vision. They started with a bunch of pretrained models and over the years added more readily available models and the ability to train custom models.

Their product is mainly aimed at developers, which is reflected in the large variety of pretrained models they propose and the effort they’ve put into integration with various programming languages and SDK.

Unfortunately, today they have a hard time putting themselves on the same level as the other actors. The main computer vision tasks are single-label classification (single possible class at the image level), multi-label classification (several possible classes at the image level), detection (object localization with bounding box), and segmentation (object localization at the pixel level). Clarifai only offer classification and tagging projects, when the vast majority of the industry already has at least some form of detection. This can be a deal breaker for a lot of enterprise-level applications.

Besides, custom models don’t seem to be their priority. Clarifai provides a very counterintuitive annotation tool, a very basic training which leads to subpar performances.

Clarifai is recommended if you are a developer and want to build a quick hackathon app with a pretrained model and few custom modifications. More than that and it might fall short.

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.