Zebra Crossing Image Dataset
收藏资源简介:
### **This dataset is collected by DataCluster Labs. To download full dataset or to submit a request for your new data collection needs, please drop a mail to: [sales@datacluster.ai](mailto:sales@datacluster.ai)** This dataset is an extremely challenging set of over 3,000+ images of excavator vehicles from multiple construction site. These images captured and crowdsourced from over 200+ different locations, where each image is **manually reviewed and verified** by computer vision professionals at Datacluster Labs. It contains a wide variety of Zebra Crossings in the county. ### **Dataset Features** - Dataset size : 3000+ images - Captured by : Over 1000+ crowdsource contributors - Resolution : HD and above (1920x1080 and above) - Location : Captured with 200+ locations - Diversity : Various lighting conditions like day, night, varied distances, view points etc. - Device used : Captured using mobile phones in 2020-2022 - Usage : Objects on the road, self-driving vehicles etc. ### Available Annotation formats COCO, YOLO, PASCAL-VOC, Tf-Record **To download full datasets or to submit a request for your dataset needs, please ping us at [sales@datacluster.ai](sales@datacluster.ai) Visit [www.datacluster.ai](www.datacluster.ai) to know more.** **Note**: All the images are manually captured and verified by a large contributor base on DataCluster platform.
此数据集由 DataCluster Labs 收集而成。若欲下载完整数据集或提交关于您新数据收集需求之申请,敬请发送邮件至:[sales@datacluster.ai](mailto:sales@datacluster.ai)。该数据集是一组极具挑战性的挖掘机械车辆图像集合,包含超过 3,000 张图像,源自多个建筑工地。这些图像通过超过 200 个不同地点的采集和众包所得,每张图像均经 Datacluster Labs 的计算机视觉专业人士手动审查与验证。数据集中包含了该县多种多样的斑马线。 ### **数据集特性** - 数据集规模:3000+ 张图像 - 采集者:超过 1000+ 众包贡献者 - 分辨率:高清及以上(1920x1080 及以上分辨率) - 采集地点:在 200+ 个地点采集 - 多样性:各种光照条件,如白天、夜晚,不同距离,不同视角等 - 设备:2020-2022 年间使用手机采集 - 用途:道路上的物体,自动驾驶车辆等 ### **可用标注格式** COCO,YOLO,PASCAL-VOC,Tf-Record **为下载完整数据集或提交关于数据集需求之申请,敬请联系:[sales@datacluster.ai](sales@datacluster.ai)。访问 [www.datacluster.ai](www.datacluster.ai) 了解更多信息。** **注意**:所有图像均由 DataCluster 平台上的广大贡献者手动采集并验证。




