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ABOUT CHALLENGE

For several years, animal detection in the wildlife has been an area of great interest among biologists. They often study the behaviour of the animals to predict their actions. Since there are a large number of different animals, manually identifying them can be a daunting task. So, an algorithm that can classify animals based on their images can help researchers monitor them more efficiently. Also, animal detection and classification can help prevent animal-vehicle accidents, trace animal facility, prevent theft, and ensure the security of animals in zoos.

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 of deep learning to build models that can classify and differentiate between different species of animals as well.

In this dataset, we provide 19,000 images of 30 different species of animals. In the next 90 days, we challenge you to build models such that given an image, the model will predict the probability of every animal class. The animal class with the highest probability will signify that the image belongs to that animal class.

Why should you participate?

  • To learn and use the latest open-source libraries and packages
  • To learn by working on live problems because it excites you more than learning from books and tutorials!
  • To build your fan following in our community
  • Of course, grab cash prizes

Who should participate?

  • Working professionals
  • Data Science/Machine Learning enthusiasts
  • College students (if you understand basics of predictive modeling)

Tutorials

Notes

  • In order to be able to claim your prizes, your HackerEarth profile must be more than 50% complete.
  • The prizes will be disbursed before the end of December.

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PRIZES

There are great prizes to be won

1st Prize

USD 100 

2nd Prize

USD 50 

GUIDELINES

  1. When the contest is running, your output will be evaluated only for 40% of the test data. After the contest is over, your output for the remaining 60% of the test data w...
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