DenseNet Models for Tiny ImageNet Classification

23 Apr 2019  ·  Zoheb Abai, Nishad Rajmalwar ·

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.

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Datasets


Task Dataset Model Metric Name Metric Value Global Rank Benchmark
Image Classification Tiny ImageNet Classification DenseNet + Residual Networks Validation Acc 60% # 20

Methods