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Cartoonize Images using TinyML Strategies with Transfer Learning

The gradual advancements towards the development of Deep Neural Networks has ultimately led to the development of certain specialised hardware for the application of such Networks with superior performance. But, many of the consumer grade hardwares and Single Board Computers (SBCs) which are used in embedded scenarios are not (yet) computationally efficient enough to perform the execution of even an already trained model within feasible limits. Thus in this paper, we focus on the implication of Post Training Quantization (PTQ) strategies to mitigate the great computational demands arising due to evolution of complex models. As the title implies, the original model incorporates a Generative Adversarial Network to generate Cartoonized versions of provided Real-World input images. This model, in its original state, takes nearly twice as much time to process the output on a single threaded workload. Our Solution to the above stated issue involves Quantizing the pre-trained model from 32-bit Floating Point values to a minimum of 8-bit Integer Values with the addition of Transfer Learning in-general. The results from our testing show that PTQ allowed the model to be compressed to a smaller size as compared to the original novelty, making it ready to be deployed on resource-constrained environments. In addition to that, a significant increase in the Inference Engine's processing performance has also been observed on general purpose hardware.