Modified-MNIST

Dataset

The train and mnistTask folders contain the two datasets used in this project.

The images in the train are 1200 x 900 dimensional grayscale images belonging to 62 classes. These classes are digits 0-9, lower-case letters a-z, and upper-case letters A-Z.

mnistTask contains 28 x 28 dimensional grayscale images belonging to 10 classes. This dataset is riddled with a lot of label noise, i.e., the images are mislabelled.

Tasks

1. Use the train dataset to train a CNN. Use no other data source or pretrained networks, and explain your design choices during preprocessing, model building and training. Also, cite the sources you used to borrow techniques. A test set will be provided later to judge the performance of your classifier. Please save your model checkpoints.

2. Next, select only 0-9 training images from the train dataset, and use the pretrained network to train on MNIST dataset. How does this pretrained network perform in comparison to a randomly initialized network in terms of convergence time, final accuracy and other possible training quality metrics? Do a thorough analysis. Please save your model checkpoints.

3. Finally, take the mnistTask dataset. Train on this dataset and provide test accuracy on the MNIST test set, using the same test split from part 2. Train using scratch random initialization and using the pretrained network of part 1. Do the same analysis as 2 and report what happens. Try and do qualitative analysis of what's different in this dataset. Please save your model checkpoints.

Libraries, Framework and Environment

• Google Colab
• TensorFlow
• TensorFlow Dataset
• TensorFlow Addons
• NumPy