Performance Analysis of You Only Look Once Object Detection Models on Custom Datasets
This article employed the YOLO family models and trained them using the transfer learning approach on custom datasets, and found that these cutting- edge performers outperformed the competition on the Custom Dataset of Open Image Datasets V6 for various classes.
Abstract
Object detection, localization, and classification are now thought to be common study areas. The advancement of Deep Learning has aided researchers working in the field of object detection. By decreasing the model’s loss functi on, the accuracy of object detection may be boosted. Training and executing this model on a suitable GPUenabled machine can also cut down on total time. In this article, we employed the YOLO family models, namely YOLO v3, YOLO v4, and YOLO v5, and trained them using the transfer learning approach on custom datasets. These cutting- edge performers outperformed the competition on the Custom Dataset of Open Image Dataset V6 for various classes, achieving mean average precision mAP @0.5 97 %, 89 %, and 88 %, respectively. This is better than the initial trained models, which had mAP @0.5 values of 57.9 %, 43.5 %, and 33 % for YOLO v3, YOLO v4, and YOLO v5, respectively