  In this paper, we present two image classification models on the Tiny
ImageNet dataset. We built two very different networks from scratch based on
the idea of Densely Connected Convolution Networks. The architecture of the
networks is designed based on the image resolution of this specific dataset and
by calculating the Receptive Field of the convolution layers. We also used some
non-conventional techniques related to image augmentation and Cyclical Learning
Rate to improve the accuracy of our models. The networks are trained under high
constraints and low computation resources. We aimed to achieve top-1 validation
accuracy of 60%; the results and error analysis are also presented.
