Deep Learning Bootcamp - Assignment 2 - Beginners: Recognize an Animal in an Image

Can you train a machine that would tell you the name of animal?



1806 Submissions


Image recognition is a vital component in robotics such as driverless vehicles or domestic robots. Image recognition is also important in image search engines such as Google or Bing image search whereby you use rich image content to query for similar stuff. Like in Google photos where the system uses image recognition to categorize your images into things like cats, dogs, people and so on so that you can quickly search your albums for things like, “give me photos of my cat”, that's awesome.

Ever noticed how Facebook instantly recognises your friend’s face and asks you if you want to tag him in that photo? That’s image recognition. That’s just a basic example.


You are working on a robotics project where you are required to train your robot so that it can differentiate between two animals. Your task here is to build a deep learning model that helps you recognize the animal in images.

Evaluation Criteria

Submissions are evaluated using Accuracy Score.


How do we do it? 

Once you generate and submit the target variable predictions on the test dataset, your submissions will be compared with the true values of the target variable. 

The True or Actual values of the target variable are hidden on the DPhi platform so that we can evaluate your model's performance on unseen data. Finally, an accuracy score for your model will be generated and displayed.

Here is your Getting Started Notebook for Assignment 2

About the Data

The training dataset consists of 1200 medium quality animal images belonging to 2 categories: mucca (cow) and pecora (sheep). All the images have been collected from "google images" and have been checked by humans. There is some erroneous data to simulate real conditions (eg. images taken by users of your app).

Dataset Link:

There are 4 files:



You need to choose a submission file.

File Format

Your submission should be in CSV format.


This file should have a header row called 'prediction'.
Please see the instructions to save a prediction file under the “Data” tab.

To participate in this challenge either you have to create a team of atleast None members or join some team