mlsanityData to 7 Oct 2026

DenseNet Models for Tiny ImageNet Classification

arXiv 1904.10429 ↗First posted 23 Apr 2019cs.CVcited by 17

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Abstract (arXiv)

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.

Key facts. Cited by 17 papers in the map (data through 7 Oct 2026). First posted on arXiv 23 Apr 2019.

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Citing papers per year: 2021: 4; 2022: 3; 2023: 3; 2024: 4; 2025: 1; 2026: 2.

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