Styria.AI vs. other Computer Vision Services

Achieving high accuracy on academic, Imagenet, CIFAR and similar datasets is certainly a valuable academic effort, but achieving high accuracy on real-world images while maintaining blazingly fast response time is an entirely different story.

In the last 6 years, since the advent of modern convolutional neural networks, computer vision research has been exploding in academic research. There are also general computer vision APIs and solutions from some of the biggest companies in the world. But still, there are little practical uses and computer vision models that work in real life.

Achieving high accuracy on academic, Imagenet, CIFAR and similar datasets is certainly a valuable academic effort, but achieving high accuracy on real-world images while maintaining blazingly fast response time is an entirely different story. Users (and businesses) want solutions that are applicable in real uses-cases and which work fast in real-time.

Identifying business value and generating ideas for practical use of object recognition, visual search and similar functionalities is not an easy task. It is to be expected that, in the future, augmented reality and robotics will become mainstream. These areas, to be able to meet the users’ demands, will need to be accompanied by computer vision models that actually work.

Styria AI team has been developing cutting-edge technology to achieve just that. And we did it! Our models are used on classifieds portals (online 2nd hand marketplace) in Europe, and there is great demand from other industries from all around the world, that want to embrace computer vision technology in real life. Our models do not recognize giraffes and chimpanzees (for now) but are very good at recognizing everyday objects that you can typically find in the home or around it.

The everyday environment is way different from objects depicted in publically available datasets like Imagenet and CIFAR, and there are many challenges regarding the data distributions and specifics that have to be solved. But, the best way to explain this is to check some examples: https://styriaai.github.io/cloudy_vision/output/output.html

Response time mean and stdev

460% faster than the next competitor

Feeder for babies correctly recognized as equipment for kids, while other APIs focus on shape and recognize it as a seat or get confused by the wooden floor.

Styria.AI API is very well suited to recognize various small objects like mobile phones, to the finest level of details, like manufacturer and even a model.

Try demos and APIs for yourself: http://www.styria.ai/demos/

You can even compare responses with APIs from other vendors using the code from our repo: https://github.com/StyriaAI/cloudy_vision

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