Cloudtect allows the usage of cloud computing in order to allow users to implement machine learning algorithms on separate servers, without having to install any libraries or applications. This would be the first major implementation of utilizing cloud computing for these types of algorithms, especially object recognition programs.

The applications of Cloudtect vary from IoT on devices, such as security camera devices where the footage is streamed to separate servers and returned with various implementations. This could also help in the area of computer-based research, allowing models to run much quicker and more effectively. The software allows many educational functionalities from training employees within a company, or it can be utilized in classroom, anywhere in the world.

The implementation of the provided code shows a console that runs an object detection neural network (YOLO v2) from the server and sequentially returns detected bounding boxes live.

Cloud computing and machine learning are both becoming utilized in everyday tasks and in the most extreme of research areas, and the team at Cloudtect believe that it is essential that these concepts be accessible to everyone. Our build consists of the following stack: Python, JavaScript, Node.js, HTML/CSS, YOLOv2, OpenCV, TensorFlow.

Check out our main site at https://cloudtect.webflow.io/ to learn more about our product.