First challenge was setting up openCV libraries on our DeepLens and testing it. At very beginning we had some issues with debugging the Greengrass lambda function code and redeploy it without using AWS console. Another challenged we faced was making the project working end-to-end with all its components (ML code, DataStorage, APIs, Mobile application, web application, etc) with a very short amount of time we had as all of us working full-time.
Accomplishments that we're proud of
OneEye identifies faces with very high accuracy and dynamically updates new faces to the database. It was an exciting experience to build and run complex Deep Learning model on a device and we are proud to present a fully functioning solution for a yet very complicated problem.
What we learned
Working with DeepLense is both fun and educational. I gained a lot of knowledge and experience about how to use Greengrass and AWS IOT by implementing the use cases. I also learned that technology is growing so fast and these kind of projects/hackathons allows us to keep up to date.
Customer detection and missing person scenarios are existing real world cases which needs an urgent lift and modifications. This DeepLens project is a start to disrupt current ways of doing this. Next step for OneEye would be finding sponsors to make OneEye available for these use cases.