  In today's world, image processing plays a crucial role across various
fields, from scientific research to industrial applications. But one
particularly exciting application is image captioning. The potential impact of
effective image captioning is vast. It can significantly boost the accuracy of
search engines, making it easier to find relevant information. Moreover, it can
greatly enhance accessibility for visually impaired individuals, providing them
with a more immersive experience of digital content. However, despite its
promise, image captioning presents several challenges. One major hurdle is
extracting meaningful visual information from images and transforming it into
coherent language. This requires bridging the gap between the visual and
linguistic domains, a task that demands sophisticated algorithms and models.
Our project is focused on addressing these challenges by developing an
automatic image captioning architecture that combines the strengths of
convolutional neural networks (CNNs) and encoder-decoder models. The CNN model
is used to extract the visual features from images, and later, with the help of
the encoder-decoder framework, captions are generated. We also did a
performance comparison where we delved into the realm of pre-trained CNN
models, experimenting with multiple architectures to understand their
performance variations. In our quest for optimization, we also explored the
integration of frequency regularization techniques to compress the "AlexNet"
and "EfficientNetB0" model. We aimed to see if this compressed model could
maintain its effectiveness in generating image captions while being more
resource-efficient.
