Google Play icon

Garden Nerd : Flower Recognition Data Science Competition

Share
Posted August 16, 2019

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.

Image credit: Capri23auto from Pixabay

Problem statement

For several years, flower recognition in the wildlife has been an area of great interest among biologists. Recognition of flowers in environments such as forests and mountains is necessary to know whether they are extinct or not. While search engines assist in searching for a flower, they  lack the robustness because of the intra-class variation among millions of flower species.

The application of Deep Learning is rapidly growing in the field of computer vision and is helping in building powerful classification and identification models. We can leverage this power to build models that can classify and differentiate between different species of flower.

Submissions to this Challenge must be received by 09:25 PM EEST Sep 22, 2019.

Source: HackerEarth

Featured news from related categories:

Technology Org App
Google Play icon
84,049 science & technology articles

Most Popular Articles

  1. Efficiency of solar panels could be improved without changing them at all (September 2, 2019)
  2. Diesel is saved? Volkswagen found a way to reduce NOx emissions by 80% (September 3, 2019)
  3. The famous old Titanic is disappearing into time - a new expedition observed the corrosion (September 2, 2019)
  4. The Time Is Now for Precision Patient Monitoring (July 3, 2019)
  5. Europe and US are Going to Try and Deflect an Asteroid (September 6, 2019)

Follow us

Facebook   Twitter   Pinterest   Tumblr   RSS   Newsletter via Email