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YOLOv5 提升小目标识别能力 解决VisDrone2019数据集_yolov5测试visdrone

yolov5测试visdrone

VisDrone2019是一个无人机航拍数据集,图像分辨率高达2000x1500。训练集包含6,471张图像,共有343,205个标签,每张图像平均包含53个实例对象,这表明对象密度很高,而且大多数对象都非常小(<32像素),如图5所示。验证集和测试集分别包含548张和1,610张图像。该数据集包括10个类别:行人、人群、自行车、汽车、货车、卡车、三轮车、遮阳三轮车、公共汽车和摩托车。本文中的所有模型都是在训练集上进行训练,然后在验证集上进行验证,最后在测试集上进行评估的。
The VisDrone Dataset is a large-scale benchmark created by the AISKYEYE team at the Lab of Machine Learning and Data Mining, Tianjin University, China. It contains carefully annotated ground truth data for various computer vision tasks related to drone-based image and video analysis.

VisDrone is composed of 288 video clips with 261,908 frames and 10,209 static images, captured by various drone-mounted cameras. The dataset covers a wide range of aspects, including location (14 different cities across China), environment (urban and rural), objects (pedestrians, vehicles, bicycles, etc.), and density (sparse and crowded scenes). The dataset was collected using various drone platforms under different scenarios and weather and lighting conditions. These frames are manually annotated with over 2.6 million bounding boxes of targets such as pedestrians, cars, bicycles, and tricycles. Attributes like scene visibility, object class, and occlusion are also provided for better data utilization.

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