Impact of Image Resizing on Deep Learning Detectors for Training Time and Model Performance
Lecture Notes in Electrical Engineering, ISSN: 1876-1119, Vol: 866 LNEE, Page: 10-17
2022
- 23Citations
- 101Captures
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Example: if you select the 1-year option for an article published in 2019 and a metric category shows 90%, that means that the article or review is performing better than 90% of the other articles/reviews published in that journal in 2019. If you select the 3-year option for the same article published in 2019 and the metric category shows 90%, that means that the article or review is performing better than 90% of the other articles/reviews published in that journal in 2019, 2018 and 2017.
Citation Benchmarking is provided by Scopus and SciVal and is different from the metrics context provided by PlumX Metrics.
Conference Paper Description
Resizing images is a critical pre-processing step in computer vision. Principally, deep learning models train faster on small images. A larger input image requires the neural network to learn from four times as many pixels, and this increase the training time for the architecture. In this work, we presented the evolution of effects of image resizing on model training time and performance. This study is applied on a vehicle dataset. We used You Look Only Once based architectures which include YOLOv2, YOLOv3, YOLOv4, and YOLOv5 with pretrained models to perform object detection. YOLO is designed to detect objects with high accuracy and high speed, which is an advent for real-time applications. Data augmentation method is used in this research to reduce overfitting problems, which approximates the data probability by manipulating the input samples. The experimental results show that if the input image size varies, then it has effects on the training time of the CNN based images classification. Additionally, this research reviewed image resizing and its impacts on the models’ performance in terms of accuracy, precision, and recall.
Bibliographic Details
http://www.scopus.com/inward/record.url?partnerID=HzOxMe3b&scp=85128713549&origin=inward; http://dx.doi.org/10.1007/978-3-030-95498-7_2; https://link.springer.com/10.1007/978-3-030-95498-7_2; https://dx.doi.org/10.1007/978-3-030-95498-7_2; https://link.springer.com/chapter/10.1007/978-3-030-95498-7_2
Springer Science and Business Media LLC
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