This project is an Image classifier based on Convulational Neural Networks which classifies an input image as a food item.
There are total 61 different food items and we have to report for a given test set (in this case of size 484) the food item displayed in it. We are given 9323 samples to train our classifier with. The output of this code is a .csv file containing the label(representing the 61 food items) corresponding to each test case of the 484 test samples. Dataset is available at https://www.aicrowd.com/challenges/chunin-exams-food-track-cv-2021.
The model is implemented using PyTorch framework in python.