  Deep Learning has revolutionized the fields of computer vision, natural
language understanding, speech recognition, information retrieval and more.
Many techniques have evolved over the past decade that made models lighter,
faster, and robust with better generalization. However, many deep learning
practitioners persist with pre-trained models and architectures trained mostly
on standard datasets such as Imagenet, MS-COCO, IMDB-Wiki Dataset, and
Kinetics-700 and are either hesitant or unaware of redesigning the architecture
from scratch that will lead to better performance. This scenario leads to
inefficient models that are not suitable on various devices such as mobile,
edge, and fog. In addition, these conventional training methods are of concern
as they consume a lot of computing power. In this paper, we revisit various
SOTA techniques that deal with architecture efficiency (Global Average Pooling,
depth-wise convolutions & squeeze and excitation, Blurpool), learning rate
(Cyclical Learning Rate), data augmentation (Mixup, Cutout), label manipulation
(label smoothing), weight space manipulation (stochastic weight averaging), and
optimizer (sharpness aware minimization). We demonstrate how an efficient deep
convolution network can be built in a phased manner by sequentially reducing
the number of training parameters and using the techniques mentioned above. We
achieved a SOTA accuracy of 99.2% on MNIST data with just 1500 parameters and
an accuracy of 86.01% with just over 140K parameters on the CIFAR-10 dataset.
