Research Article
Image Classification with Transfer Learning and FastAI
@INPROCEEDINGS{10.1007/978-3-030-89814-4_59, author={Ujwal Gullapalli and Lei Chen and Jinbo Xiong}, title={Image Classification with Transfer Learning and FastAI}, proceedings={Mobile Multimedia Communications. 14th EAI International Conference, Mobimedia 2021, Virtual Event, July 23-25, 2021, Proceedings}, proceedings_a={MOBIMEDIA}, year={2021}, month={11}, keywords={Transfer learning Deep learning Image classification}, doi={10.1007/978-3-030-89814-4_59} }- Ujwal Gullapalli
Lei Chen
Jinbo Xiong
Year: 2021
Image Classification with Transfer Learning and FastAI
MOBIMEDIA
Springer
DOI: 10.1007/978-3-030-89814-4_59
Abstract
Today deep learning has provided us with endless possibilities for solving problems in many domains. Diagnosing diseases, speech recognition, image classification, and targeted advertising are a few of its applications. Starting this process from scratch requires using large amounts of labeled data and significant cloud processing usage. Transfer learning is a deep learning technique that solves this problem by making use of a model that is pre-trained for a certain task and using it on a different task of a related problem. Therefore, the goal of the project is to utilize transfer learning and achieve near-perfect results using a limited amount of data and computation power. To demonstrate, an image classifier using FastAI that detects three types of birds with up to 94% accuracy is implemented. This approach can be applied to solve tasks that are limited by labeled data and would gain by knowledge learned from a related task.
