Flowers

Practice Problem: Intel Scene Classification Challenge

Online 05-10-2018 12:00 PM to 31-03-2019 11:59 PM
  • 3013

    Registered

  • Total prize money: INR 75,000

    Prizes

There has been a tremendous increase in the applications of computer vision. The applications of CV range from smart cameras and video surveillance to robotics, transportation and more.

Intel remains at the forefront to enable developers and data scientists build useful CV applications optimisedfor Intel processor architecture.

We encourage the participants to use the new OpenVINO™ toolkit by Intel. Short for Open Visual Inference & Neural Network Optimisation, the OpenVINO™ toolkit (formerly Intel® CV SDK) contains optimisedOpenCV and OpenVX libraries, deep learning code samples, and pre-trained models to enhance computer vision development. It’s validated on 100+ open source and custom models, and is available absolutely free. You can get started with the toolkit from the resources provided at thislink

Prize:

  • 1st: Rs. 35,000
  • 2nd: Rs. 25,000
  • 3rd: Rs. 15,000

Rules

  • One person cannot participate with more than one user accounts.
  • You are free to use any tool and machine you have rightful access to.
  • You are free to use solution checker as many times as you want.

FAQs

  • Can I share my approach/code?

    Absolutely. You are encouraged to share your approach and code file with the community. There is even a facility at the leaderboard to share the link to your code/solution description.

  • I am facing a technical issue with the platform/have a doubt regarding the problem statement. Where can I get support?

    Post your query on discussion forum at the thread for this problem, discussion threads are given at the bottom of this page. You could also join the AV slack channel by clicking on 'Join Slack Live Chat' button and ask your query at channel: practice_problems.

Please register to participate in the contest

Please register to participate in the contest

Please register to participate in the contest

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