  This research explores the realm of neural image captioning using deep
learning models. The study investigates the performance of different neural
architecture configurations, focusing on the inject architecture, and proposes
a novel quality metric for evaluating caption generation. Through extensive
experimentation and analysis, this work sheds light on the challenges and
opportunities in image captioning, providing insights into model behavior and
overfitting. The results reveal that while the merge models exhibit a larger
vocabulary and higher ROUGE scores, the inject architecture generates relevant
and concise image captions. The study also highlights the importance of
refining training data and optimizing hyperparameters for improved model
performance. This research contributes to the growing body of knowledge in
neural image captioning and encourages further exploration in the field,
emphasizing the democratization of artificial intelligence.
