Vehicle-Rear is a novel dataset for vehicle identification that contains more than three hours of high-resolution videos, with accurate information about the make, model, color and year of nearly 3,000 vehicles, in addition to the position and identification of their license plates.
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This dataset is a collection of 4,000 images of cars in multiple scenes that are ready to use for optimizing the accuracy of computer vision models. All of the contents is sourced from PIXTA's stock library of 100M+ Asian-featured images and videos. PIXTA is the largest platform of visual materials in the Asia Pacific region offering fully-managed services, high quality contents and data, and powerful tools for businesses & organizations to enable their creative and machine learning projects. For more details, please refer to the link: https://www.pixta.ai/ Or send your inquiries to contact@pixta.ai
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Vehicle-1M involves vehicle images captured across day and night, from head or rear, by multiple surveillance cameras installed in cities. There are totally 936,051 images from 55,527 vehicles and 400 vehicle models in the dataset. Each image is attached with a vehicle ID label denoting its identity in real world as well as a vehicle model label indicating the make, model and year of the vehicle(i.e. "Audi-A6-2013"). All publications using Vehicle-1M dataset should cite the paper below: Haiyun Guo, Chaoyang Zhao, Zhiwei Liu, Jinqiao Wang, Hanqing Lu: Learning coarse-to-fine structured feature embedding for vehicle re-identification. AAAI 2018.