By Nnamdi Odumody
Moses and John Olafenwa, the CEO and CTO of AI Commons (now called DeepQuest AI), respectively, launched Deep Quest AI to advance artificial intelligence and make it accessible to every individual and organization in the world.
They created Deepstack, an AI server which can be easily installed, used completely offline or on the cloud for facial recognition, object detection, scene recognition and custom recognition for enterprise, consumer and security applications. Its API allows you to run thousands to millions of requests without pay as you use costs, and provides the perfect integration channel for all your applications. Also, it makes it easier to add new recognition APIs at will, with capacity to deploy instantly with strong user privacy.
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Many developers around the world currently use their solutions and they provide extensive and comprehensive tutorials for developers, machine and deep learning engineers and researchers. Their technologies, tools and knowledge are available to individuals, teams, organizations and institutions across the globe in English, Chinese and French languages.
Through ImageAI, a game changing product from their stables, they have created a computer vision library which empowers developers to easily integrate state of the art artificial intelligence features into their new and existing applications and systems. This solution is used by many developers, students, researchers, tutors and experts in corporate organizations.
ImageAI is a python library built to empower developers to independently build applications and systems with self-contained Computer Vision capabilities. Built with simplicity in mind, ImageAIsupports a list of state-of-the-art Machine Learning algorithms for image prediction, custom image prediction, object detection, video detection, video object tracking and image predictions trainings. ImageAI currently supports image prediction and training using 4 different Machine Learning algorithms trained on the ImageNet-1000 dataset. ImageAI also supports object detection, video detection and object tracking using RetinaNet trained on COCO dataset. Eventually, ImageAI will provide support for a wider and more specialized aspects of Computer Vision including and not limited to image recognition in special environments and special fields.
It provides API to recognize 1000 different objects in a picture using pre-trained models that were trained on the Image Net-1000 data set. The model implementations provided are SqueezeNet, ResNet, Inception V3 and DenseNet. It also provides API to detect, locate and identify 80 most common objects in everyday life in a picture. Also, its extended API helps to detect, locate and identify 80 objects in videos and retrieve full analytical data on every frame, second and minute. This feature is supported for video files, device camera and IP camera live feed. New image recognition models on new image datasets for custom use cases can also be trained with its API and also provides implementations to integrate and deploy the custom image recognition models.
Another game changing product is Torch Fusion, a modern framework built to accelerate research and development of advanced deep learning systems. It provides a complete platform for loading datasets, defining metrics and models and training them with extensible trainers that can be seamlessly customized with custom training logic all with full support for training on both CPUs and GPUs. Torch Fusion provides applications and extensible specialized trainers for a wide range of conditional and unconditional Generative Adversarial Networks which are state of art for image generation.
All Together
Africa and especially Nigeria must support inventors like Moses and John Olafenwa to scale their missions and position the continent, solidly, on the path of AI future. Heralded to be as impactful as the invention of fire, AI will have real implications for markets and industries. Young people like Moses and John offer huge promises, and must be supported.
Prof, thank you for engaging the two AI brothers!
Thanks Akeem
It is great that Facial verification is being used for identity verification of users as there is no more effective method to fight online fraud and identity-based scams except for AI-based identity verification services. There are several of them using machine learning to not only verify identity through facial verification but businesses also use authentic identity document in order to verify the true identity of their incoming users.
Great – they are working in that space and that is awesome.
Where are Moses and John Olafenwa based – Nigeria, China, US? What is their background? Which companies are currently using their products or applications?
Both are based in Lagos. On companies using their products, click their website and link to GitHub, they have thousands of users. GitHub is a public code repository. s!
I was most impressed by their tutorials and book which provide an African context to learning & practising AI. Immediately listed it on our page that seeks to aggregate this kind of material with African context http://alliance4ai.org/learn/ , https://deepquestai.com/education/ . Should be less difficult to get them in US-based accelerators going forward with this progress.