Description
Problem definition
In this mini-project, we will develop models to classify image data. We will use a ”modified” MNIST dataset: https://drive.google.com/file/d/1LcKqf1d7bctw5lx0YZf31kCUF0zEYOsi/view?usp=sharing where each image contains 1-5 digits. The task is to implement a model for this multi-label classification task.
If the number of digits in an image is less than 5 then the remaining labels are associated with a special class called ”no-digit”. As can be seen in the figure-1, no-digit is class ”10”. This means we have a total of 11 classes (0-10 digits and no-digit), and each image is associated with 5 such classes. The dataset contains both training and test data, where the label for the test set is not provided.
You are free to use any Python libraries you like to extract features and pre-process the data, evaluate your model, and to tune the hyper-parameters, etc. Your model should be a neural network and it should be trained by automatic differentiation.
