简介 介绍一下目标检测领域另外一个比较有名的数据集 MS COCO (Microsoft COCO: Common Objects in Context) . MSCOCO 数据集是微软构建的一个数据集,其包含 detection, segmentation, keypoints等任务。 MSCOCO主要是为了解决detecting non-iconic views of ob... ...
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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 Dollár Full-Text Cite this paper Add to My Lib Abstract: We present a new dataset with the goal of advancing th...
common objects in their natural context. Objects are labeled using per-instance segmentations to aid in precise object localization. Our dataset contains photos of 91 objects types that would be easily recognizable by a 4 year old. With a total of 2.5 million labeled instances in 328k images,...
Fig9右,在COCO上测试,正确预测的平均交并比也都不高,基本不超0.5,这一点也不是很懂 讨论 又强调了一些东西 over 70,000 worker hours non-iconic images of objects in natural environments and varied viewpoints. rich contextual information 未来展望/方向: ...
common objects in their natural context. Objects are labeled using per-instance segmentations to aid in precise object localization. Our dataset contains photos of 91 objects types that would be easily recognizable by a 4 year old. With a total of 2.5 million labeled instances in 328k images,...
COCO is provided by Mircrosoft.本数据集由微软提供。 数据列表 数据名称上传日期大小下载 cocoapi-master.zip2021-02-281.53MB coco_image_info_test2017.zip2021-02-281.09MB 文档 COCO:Common Objects in Context环境常见物体数据集COCO 1.Overview The MS COCO (Microsoft Common Objects in Context) dataset is...
FS-COCO: Towards Understanding of Freehand Sketches of Common Objects in ContextWe advance sketch research to scenes with the first dataset of freehand scene sketches, FS-COCO. With practical applications in mind, we collect sketches that convey scene content well but can be sketched within a ...
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...
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...