This page shows up underneath the data files.
File descriptions
wiki_labeled
- the labeled dataset, and should be used to train and test the model. The wiki_labeled/ directory will contains 60327 facial images kept in 100 folders, naming 00-99. Dimension of each image is 100 pixels by 100 pixels.
wiki_labele.csv
file, containing meta information of each of the 60327 images. It contains the following information about each of the 60327 images:
ID: identification number of the subject (starting from 2002)
dob: the date of birth of the subject. (It is Matlab’s datenum value calculated based on total number of days since January 0, 0000.)
dob_str: the DD-MMM-YYYY format dob value.
photo_taken: when the photo was taken (only the year  value)
full_path: directory path, including filename of the image
gender: Gender of the subject (0: female, 1: male, NaN if unknown)
name: name of the subject
face_location: location of the face.
face_score: detector score (the higher the better). Inf implies that no face was found in the image, and the face_location then just returns the entire image.
second_face_score: detector score of the face with the second highest score. This is useful to ignore images with more than one face. second_face_score is NaN (not a number) if no second face was detected.
age: age of the person (in years), and was calculated based on the “dob” value and the “photo_taken” values.
wiki_judge_images
containing **1409** facial images (1.png, etc.) of resolution 100x100. This is the judge/test dataset for you based on what you are going to apply your learned model to predict ages of the **1409** subjects.
wiki_judge.csv
contains some meta information about the **1409** images. For instance,  ID, full_path, gender, face_score, second_face_score. However, the information does not include “age” field.
sample_submission.csv
- a sample submission file in the correct format. Please disregard the values in the age field. They are all made up values.