DenseNet Models for Tiny ImageNet Classification

04/23/2019 ∙ by Zoheb Abai, et al. ∙ 0

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



There are no comments yet.


page 2

page 4

Code Repositories


This repository contains the jupyter notebooks for the custom-built DenseNet Model build on Tiny ImageNet dataset

view repo
This week in AI

Get the week's most popular data science and artificial intelligence research sent straight to your inbox every Saturday.