  Convolutional networks are at the center of best-in-class computer vision
applications for a wide assortment of undertakings. Since 2014, a profound
amount of work began to make better convolutional architectures, yielding
generous additions in different benchmarks. Albeit expanded model size and
computational cost will, in general, mean prompt quality increases for most
undertakings but, the architectures now need to have some additional
information to increase the performance. I show evidence that with the
amalgamation of content-based image similarity and deep learning models, we can
provide the flow of information which can be used in making clustered learning
possible. The paper shows how training of sub-dataset clusters not only reduces
the cost of computation but also increases the speed of evaluating and tuning a
model on the given dataset.
