π΅οΈ Introduction
Instructions
Introduce the challenge here.
Include :
- A picture
- A catchy one-liner introducing the problem
- A link to the getting started code
- Do not remove the contribution line.
For example, this is how we write this section for the MNIST challenge
Our writers were tired of introducing problems to you, well we decided to give them a break! Voila, the one problem which needs no introduction! life started by learning numbers, well you've come a full circle! let's get back to it !
We give you the very famous MNIST dataset of handwritten digits, can you identify them?
Understand with code! Here is getting started code for you.π
πΎ Dataset
Instructions
Write something about the dataset here :
- What the dataset is about?
- What are the attributes of the dataset?
- How many attributes are there in the dataset?
- What is the data type of the attributes?
- What are the classes that need to be predicted?
- Formatting Instructions
For example, this is how we write this section for the MNIST challenge
The MNIST database (Modified National Institute of Standards and Technology database) is a large database of handwritten digits that is commonly used for training various image processing systems. The database is also widely used for training and testing in the field of machine learning. Each image is 28
pixels in height and 28
pixels in width, for a total of 784
pixels in total. Each pixel has a single pixel-value associated with it, indicating the lightness or darkness of that pixel, with higher numbers meaning darker. This pixel-value is an integer between 0
and 255
, inclusive.
For simplification, images have been stored in the CSV file. The train.csv
has 785
columns, the first column is the label and the rest 784
contain the pixel value of the associated image pixel.
π Files
Instructions
Describe the file structure for this challenge :
- Add all the relevant data files in the 'Resources' section of this challenge.
- Provide brief explationations for the files uploaded.
For example, this is how we write this section for the MNIST challenge
Following files are available in the resources
section:
train.csv
- (60000
samples) This csv file contains the pixel values as columns along with the digits it represent.test.csv
- (10000
samples) File that will be used for actual evaluation for the leaderboard score and it does not have the digit represented by the pixel values.
π Submission
Instructions
Submission instructions :
- Replace the header and the range of predicted value below according to the dataset.
For example, this is how we write this section for the MNIST challenge
- Prepare a csv containing header as
label
and predicted value as digit[0-9]
with name assubmission.csv
. - Sample submission format available at
sample_submission.csv
.
π Evaluation Criteria
Instructions
Description of the evaluation criteria :
- Explain the formula/method of the evaluation criteri being used to score the submissions.
- Add an image of the mathematical formula of the evaluation criteria. You can use this link to generate the image.
For example, this is how we write this section for the MNIST challenge
During evaluation F1 score will be used to test the efficiency of the model where,
π± Contact
- Your contact Information
π References
Instructions
References:
- Add the source from where the dataset was taken.
- Any additional acknowledgments asked by the source.
Participants

