Content

There are many species of flowers in the world. Some species have many colors, such as roses, and tulips. It is hard to remember the names of all the flowers, and their information. Furthermore, there are some flowers from similar species that can confuse anybody. For example, white Champaka and Champak have similar names and petal shapes, but they have different colors and petal lengths.

Problem Statement

Babu Rao, the owner of a flower boutique, is a person who faces difficulty in recalling the names of the flowers. He remembers that he had practised deep learning during his college days which can help him in building a machine that would recognize the different flowers. But he hardly remembers all the tools to be used. So he seeks help from his old friend, that is you. Will you help him build a deep learning model?

Usually he sells 5 different flower collections - daisy, dandelion, roses, sunflowers and tulips.

Objective

You are required to build a machine learning model that would recognize the name of the flower.

What you will learn?

Practical applications of Deep Learning Algorithms, optimizing neural networks, CNN, etc. (Learn it here)

Evaluation Criteria

Submissions are evaluated using Accuracy Score.

How do we do it?

Once we release the data, anyone can download it, build a model, and make a submission. We give competitors a set of data (training data), with both the independent and dependent variables.

We also release another set of data (test dataset) with just the independent variables, and we hide the dependent variable that corresponds with this set. You submit the predicted values of the dependent variable for this set and we compare it against the actual values.

The predictions are evaluated based on the evaluation metric defined in the datathon.

Data Sprint #25: Flower Recognition

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