  Interest in the research areas related to meme propagation and generation has
been increasing rapidly in the last couple of years. Meme datasets available
online are either specific to a context or contain no class information. Here,
we prepare a large-scale dataset of memes with captions and class labels. The
dataset consists of 1.1 million meme captions from 128 classes. We also provide
reasoning for the existence of broad categories, called "themes" across the
meme dataset; each theme consists of multiple meme classes. Our generation
system uses a trained state-of-the-art transformer-based model for caption
generation by employing an encoder-decoder architecture. We develop a web
interface, called Memeify for users to generate memes of their choice, and
explain in detail, the working of individual components of the system. We also
perform a qualitative evaluation of the generated memes by conducting a user
study. A link to the demonstration of the Memeify system is
https://youtu.be/P_Tfs0X-czs.
