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物体检测–COCO数据集COCO数据集介绍 微软团队提供:http://mscoco.org/ ECCV Workshops:MicrosoftCOCO:CommonObjectsinContext从复杂的日常场景中截取,包括91类目标,328000影像和2500000个label 种类更丰富 单个物体数量更多 所以评价算法性能是COCO数据集要更低一些。COCO数据集 ...
Microsoft COCO: Common Objects in Context Tsung-Yi Lin, Michael Maire, Serge Belongie, James Hays, Pietro Perona, Deva Ramanan, Piotr Dollar, Larry Zitnick ECCV|September 2014 Published by European Conference on Computer Vision Publication
We present a new dataset with the goal of advancing the state-of-the-art in object recognition by placing the question of object recognition in the context of the broader question of scene understanding. This is achieved by gathering images of complex ev
和ImageNet对比,COCO有更少类别,但每个类别有更多实例,更有利于目标的定位 和ImageNet、VOC、SUN相比,该数据集每个类别都有更多实例,更关键的是每张图中实例更多,有利于学习目标间的关系 和ImageNet相比、VOC相比,该数据集每张图里的实例更多;SUN一张图里的实例则比该数据集高,但整体上数据集中的实例更少。
We present a new dataset with the goal of advancing the state-of-the-art in object recognition by placing the question of object recognition in the context of the broader question of scene understanding. This is achieved by gathering images of complex ev
COCO数据集:本数据集包含了91种物体类型的图像,这些物体类型能够被4岁大小的孩子豪不费力的识别出来。数据集有32.8万张图片,包含有250万个标注实例。 标注工具:Microsoft自研 类别确定使用多个数据源来建立顶…
Microsoft COCO: Common Objects in Context Tsung-Yi Lin Michael Maire Serge Belongie Lubomir Bourdev Ross Girshick James Hays Pietro Perona Deva Ramanan C. Lawrence Zitnick Piotr Doll ´ ar Abstract—We present a new dataset with the goal of advancing the state-of-the-art in object recognition...
COCO（MicrosoftCOCO：CommonObjectsinContext）论⽂阅 读笔记 以下为我总结出的论⽂各部分的主要内容，某些地⽅可能夹带着⼀些⾃⼰的理解和思考。⽬录 摘要 该论⽂/该数据集的⽬的 推动⽬标识别领域的技术突破 数据集概要 320k张图⽚，250万个实例，91种实例类型 标注类型：实例分割 该数据集...
为了更好的介绍这个数据集,微软在ECCV Workshops里发表这篇文章:Microsoft COCO: Common Objects in Context。从这篇文章中,我们了解了这个数据集以scene understanding为目标,主要从复杂的日常场景中截取,图像中的目标通过精确的segmentation进行位置的标定。图像包括91类目标,328,000影像和2,500,000个label。