YOLOv8
Dig21000 is a comprehensive dataset consisting of 21,000 images of digit-based rotary meters photographed in uncontrolled environments.
It integrates images from open datasets on the Roboflow platform and those independently collected.
After undergoing cleaning, the images are randomly divided into training and testing sets at a ratio of 6:1.
It contains 10 digit categories. The images are affected by multiple factors and can be used for research on digital dial recognition. It is publicly available and citation is required when using it.
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This is the collection of the Ecuadorian Traffic Officer Detection Dataset. This can be used mainly on Traffic Officer detection projects using YOLO. Dataset is in YOLO format. There are 1862 total images in this dataset fully annotated using Roboflow Labeling tool. Dataset is split as follow, 1734 images for training, 81 images for validation and 47 images for testing. Dataset is annotated only as one class-Traffic Officer (EMOV). The dataset produced a Mean Average Precision(mAP) of 96.4 % using YOLOv3m, 99.0 % using YOLOv5x and 98.10 % using YOLOv8x.
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