Problem statement
Every new parent is sure to go leaps and bounds to provide a safe, caring, and nourishing home for their infant. With the advent of Baby Safety Month, a renowned multi-store hypermarket is all set to grow its business, by targeting new and expecting parents worldwide. As part of its expansion, the owner plans to introduce an exclusive section that will sell products for babies and toddlers. To optimise the inventory procurement process, teams across all stores were tasked to collate images of commonly used baby-products.
Your task, as a Machine Learning expert, is to build a Deep Learning model that will tag each image with the extracted product types and brand names of these products. In case there is no brand name mentioned on a product, the model should tag the image as Unnamed.
Dataset
The dataset consists of 1500 images depicting numerous baby products - baby-proofing kits, toys, gadgets, and the like.
The benefits of practicing this problem by using unsupervised Machine Learning/Deep Learning techniques are as follows:
We challenge you to build a model that will tag images with corresponding brand names of baby/kid products.
Prizes
Considering these unprecedented times that the world is facing due to the Coronavirus pandemic, we wish to do our bit and contribute the prize money for the welfare of society.
Overview
Deep Learning is an application of Artificial Intelligence (AI) that provides systems with the ability to automatically learn and improve from experience without being explicitly programmed. Deep Learning is a science that determines patterns in data. These patterns provide deeper meaning to problems and help you to first understand problems better and then solve the same with elegance. HackerEarth’s Deep Learning challenge is designed to help you improve your Deep Learning skills by competing and learning from fellow participants.
Here’s presenting HackerEarth’s Deep Learning Challenge—Keep babies safe
This challenge is designed to help you improve your Machine Learning skills by competing and learning from fellow participants.
Why should you participate?
Who should participate?
Tutorials