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About Practice Problem: Face Counting Challenge

The method of face detection in pictures is complicated because of variability present across human faces such as pose, expression, position and orientation, skin colour, the presence of glasses or facial hair, differences in camera gain, lighting conditions, and image resolution.
 
Object detection is one of the computer technologies, which connected to the image processing and computer vision and it interacts with detecting instances of an object such as human faces, building, tree, car, etc. The primary aim of face detection algorithms is to determine whether there is any face in an image or not. We challenge all the hackers to participate in this computer vision challenge that aims to test skills in deep learning and object detection.
 

Data Science Resources

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 can use any programming language or statistical software.
  • You are free to use solution checker as many times as you want.

FAQs

1.  Are there any prizes/AV Points for this contest?

This contest is purely for learning and practicing purpose and hence no participant is eligible for prize or AV points.

2. 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.

3. 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.


Get started with your first computer vision challenge to count the number of faces in the image. Don’t panic!! if you are a complete beginner we will provide you the best resources to start your computer vision journey.

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