Welcome to AI Blitz XIII! 🚀 | Starter Kit For This Challenge! 🛠| Baseline For This Challenge

Community Contribution Prizes 📓  |  Find Teammates 👯‍♀️

Discord AI Community 🎧

Overview

Age is not just a number, it is an attribution of facial features. A person’s age plays a big role in social interactions. Instagram, Snapchat and Tiktok are filled with these filters are can predict your age from an image or a short video. In this puzzle, we look under the hood to build your very own age prediction model.

🕵️ Problem Statement

Can you build an age prediction model accurately predicts the age of a human face from an input image? There are total 10 buckets, each with an age range of 10 years  (0-10, 10-20, 20-30, … 90-100). Your task is to predict the age of an input image into one of the bucket.

Example,

💪 Getting Started

Our Starter Kit comes with the random prediction using the sample submission file and our baseline uses Random Forest Classifier after resizing the images and using its pixel value for classification. You can check it out here.

💾 Dataset

The dataset is split into 3 different sets - train, validation & test set. The training & validation set will be used in training your models and the predictions generated from the test set will be used to evaluate your model.   Each set contains 5000, 2000, and 3000 samples respectively.

The training and validation set contains a zip file and a csv file. The zip file contains the face images with a .jpg extension and dimensions 512x512. The csv file contains two columns as below -

• ImageID - This column corresponds to the image file name in the zip file.   
• age - The age group of the person in the face image. This column has over 10 classes from 0-10, 10-20, 20-30 …. 90-100.   

• train.zip
  ├── f5ka8.jpg	
  ├── dk5ns.jpg
  ├── 3knds.jpg
  └── ... over 5k samples
  

• ImageID	age
  f5ka8	50-60
  dk5ns	10-20

• The test set will contain only a zip file with 3k samples for evaluation.

📁 Files

Following files are available in the resources section:

• train.zip - ( 4k samples ) This zip file contains the images for the training set.
• train.csv - ( 4k samples ) This csv file contains the labels for the training set.
• val.zip - ( 2k samples ) This zip file contains the images for the validation set.
• val.csv - ( 2k samples ) This zip file contains the labels for the validation set.
• test.zip - ( 3k samples ) This zip file contains the images for the testing set for evaluation.  
• sample_submission.csv - This csv file contains the format of your csv file for submitting the results.

🚀  Submission

Learn to make your first submission using the starter kit 🚀

• Create a submission folder in your working directory.

• Use sample_submission.csv provided in the resources section and replace the age column values with your model predictions for the corresponding ImageID column.

• Save the CSV in the submission folder as  submission.csv  
• Inside a submission directory, put the .ipynb notebook from which you trained the model and generate predictions and save it as notebook.ipynb.
• Zip the submission directory

Overall, this is what your submission directory should look like

submission
├── assets
│   └── submission.csv
└── original_notebook.ipynb

Make your first submission here 🚀 !!

🖊 Evaluation Criteria

During the evaluation, the F1 Score ( average=weighted ) as the primary score and the Accuracy Score as the secondary score will be used to test the efficiency of the model. 

📱 Contact

• Shubhamai
• Aditya Jha
• Ayush Shivani