We are witnessing a proliferation of massive visual data. Unfortunately scaling existing computer vision algorithms to large datasets leaves researchers repeatedly solving the same algorithmic and infrastructural problems. Our goal is to democratize computer vision; one should not have to be a computer vision, big data and deep learning expert to have access to state-of-the-art distributed computer vision algorithms. We provide researchers, students and developers access to state-of-art distributed computer vision and deep learning algorithms as a cloud service through Web Interface & APIs.

A recent World Economic Form report and a New York Times article declared data to be a new class of economic asset, like currency or gold. Visual content is arguably the fastest growing data on the web. Photo-sharing websites like Flickr and Facebook now host more than 6 and 90 Billion photos (respectively). Besides consumer data, diverse scientific communities (Civil & Aerospace Engineering, Computational Biology, Bioinformatics, and Astrophysics, etc) are also beginning to generate massive archives of visual content, without necessarily the expertise or tools to analyze them.

Moreover, designing and implementing efficient and provably correct computer vision algorithms is extremely challenging. Researchers must repeatedly solve the same low-level problems: building & maintaining a cluster of machines, formulating each component of the computer vision pipeline, designing new deep learning layers, writing custom hardware wrappers etc. We are building CloudCV, an ambitious system that will contain algorithms for end-to-end processing of image & video content. Users will be able to upload their own code and convert it into a cloud service with minimal effort so that anybody can implement, use or just play around with the algorithm online or through API endpoints without having to worry about infrastructural and implementation related issues.