A METHOD FOR PROBLEM SOLVING FOGGY CITYSCAPES IMBALANCE DATASET
Abstract
Imbalance dataset is the major problem we all will face in the process of developing deeplearning model. There were many approaches to solve this very problem such as heuristic data
sampling and modifying loss function for model training. In order to find the solution, we chose
Foggy Cityscapes dataset for the experiment since this dataset has imbalance object class distribution.
We proposed a method to solve imbalance dataset namely instance level downsampling as an
extension of traditional downsampling method. The algorithm of this method will selectively keep
and drop certain image in the dataset by evaluating the majority and minority object class proportion
inside a single image. After comparing the model evaluation using Mean Average Precision (mAP)
metric, the model which was trained with balanced dataset has more balanced knowledge or less
biased across the object classes of interest.
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References
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Klasifikasi Gizi Balita. Jember, Indonesia. Universitas Muhammadiyah Jember.
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[Online] Available at https://www.analyticsvidhya.com/blog/2020/07/10-techniques-to-dealwith-class-imbalance-in-machine-learning/ [accessed on November 28th, 2021].
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9adec8f390f1 [accessed on November 29
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IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR).
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https://github.com/tensorflow/models.
Published
2022-07-13
How to Cite
WIBOWO, suryo adhi.
A METHOD FOR PROBLEM SOLVING FOGGY CITYSCAPES IMBALANCE DATASET.
Jurnal Elektro dan Telekomunikasi Terapan (e-Journal), [S.l.], v. 9, n. 1, p. 1138 - 1144, july 2022.
ISSN 2442-4404.
Available at: <//journals.telkomuniversity.ac.id/jett/article/view/4414>. Date accessed: 27 apr. 2024.
doi: https://doi.org/10.25124/jett.v9i1.4414.
Section
MULTIMEDIA
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