  Modern Neural Networks are eminent in achieving state of the art performance
on tasks under Computer Vision, Natural Language Processing and related
verticals. However, they are notorious for their voracious memory and compute
appetite which further obstructs their deployment on resource limited edge
devices. In order to achieve edge deployment, researchers have developed
pruning and quantization algorithms to compress such networks without
compromising their efficacy. Such compression algorithms are broadly
experimented on standalone CNN and RNN architectures while in this work, we
present an unconventional end to end compression pipeline of a CNN-LSTM based
Image Captioning model. The model is trained using VGG16 or ResNet50 as an
encoder and an LSTM decoder on the flickr8k dataset. We then examine the
effects of different compression architectures on the model and design a
compression architecture that achieves a 73.1% reduction in model size, 71.3%
reduction in inference time and a 7.7% increase in BLEU score as compared to
its uncompressed counterpart.
