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Download PDFOpen PDF in browserPerformance Comparison of YOLOv7 and YOLOv8 Using the YCB Datasets YCB-M and YCB-VideoEasyChair Preprint 13111, version 211 pages•Date: May 20, 2024AbstractIn this paper, the two frameworks YOLOv7 and YOLOv8 are compared using two labeled YCB datasets YCB-M and YCB-Video. We provide an additional dataset, called Robot Domain Dataset (RDD), to evaluate the performance of the two YOLO frameworks on a new data domain, to simulate situations were it is not possible to retrain the models due to a lack of data or time. Furthermore, the impact of different amounts of training data on performance is observed. For comparability, the training and validation pipelines are provided. We were able to show that both frameworks perform very well on the datasets we retrained on. But on our new dataset YOLOv7 significantly outperforms YOLOv8 by 22% mean average precision. The division of the datasets, the code of the training and validation pipelines, the trained models and the dataset RDD can be found at https://github.com/iki-wgt/yolov7_ yolov8_benchmark_on_ycb_dataset Keyphrases: MS COCO, Service Robotics, YCB Dataset, YOLO, benchmark, map, object detection Download PDFOpen PDF in browser |
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