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现有的驾驶数据集在视觉内容和支持的任务方面不足,无法研究自动驾驶的多任务学习。研究人员通常被限制在一个数据集上研究一小组问题,而现实世界的计算机视觉应用程序需要执行各种复杂的任务。作者构建了驾驶视频数据集 BDD100K,包含 10 万个视频和 10 个任务,以评估图像识别算法在自动驾驶方面的令人兴奋的进展。该数据集具有地理、环境和天气多样性。
本数据集选取BDD100K中的实例分割部分,包含训练集验证集和注释。文件夹格式如下:
@InProceedings{bdd100k,
author = {Yu, Fisher and Chen, Haofeng and Wang, Xin and Xian, Wenqi and Chen, Yingying and Liu, Fangchen and Madhavan, Vashisht and Darrell, Trevor},
title = {BDD100K: A Diverse Driving Dataset for Heterogeneous Multitask Learning},
booktitle = {The IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},
month = {June},
year = {2020}
}
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