0. Background and motivation

Motivation

Pitch for MealPic

Deep Images Hub. It is a platform that inspired by my previous venture MealPic. MealPic was a startup to try to use computer vision, augmented reality and personalized recommendation to help pregnant women to identify packaged food that meet both their craving and personal health. During our customer discovery and market researching, we discovered that consumer products and services use cases for using computer vision to recognize objects on mobile and IoT is on great demands. Even Amazon is trying to tap into this market by pushing their AWS Deeplens with their cloud services.

However, in order to train a computer vision model that can correctly recognize the object under all kind of condition of lighting and background environment, we need a large volume of diversified images for a single object.

For example, to train a good quality computer vision model required a lot of image data. For example if a we want to train a computer vision model to recognize this Lemon Flavor of LaCroix Sparkling Water. The data scientist will need approximately 1000+ images of the sparkling water under all kind of lighting and background environment. The problem is, where can the we find these many of images? Even the internet does have these many of images of the same product.

Can’t imagine to train a model to recognize different soft drinks on the market.

Without image datasets, model can’t be train and new computer vision business like MealPic can’t be create. As a data engineer with a heart of an entrepreneur. I spotted this problem and try to come up with a solution with Deep Images Hub.

Deep Images Hub. Deep Images Hub is the centralized image data hub that provides crowdsourced labeled images from images suppliers to data scientist. Data Scientist can download the images by their choices of labels. Additionally for premium users, they can request to train a baseline model. So Deep Image Hub is the automated platform for computer vision.

More details can be found in this more in-depth slides: Slides