Deepomatic or Microsoft Custom Vision : Which one to choose ?

Microsoft Custom Vision 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.

Microsoft is part of those leaders and took the approach to simplify training to the maximum. You either choose a cloud or edge model, and then you have two options: a simple training for quick results or a throughout AutoML training where different models and parameters are tried in order to find the best models.

Their annotation interface while still a bit crude is going in the right direction. For instance, you can start to use your trained model to speed up annotation by suggesting bounding boxes for detection. This alone can drastically reduce annotation time. There are still some quirks to make the annotation phase truly smooth but this was the best annotation interface out of BigTech.

Where we found them to be lacking was their edge deployment capabilities. There is a big performance gap between their cloud and edge models. Besides, their edge deployment is just a simple python Flask API. If you need anything serious, you will have to code it yourself.

Microsoft Custom Vision provides fairly good annotation and automatic training capabilities but is a bit more flimsy when it comes to edge deployment. The main concern was their model performances which were subpar for most projects and didn’t allow for enterprise-level applications where every percent matters. When you want to replace an existing workflow you need to be at least as good and even better for easy adoption.

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.