MSCOCO2017数据集

COCO数据集是一个大型数据集，全称是Common Objects in Context，是微软团队提供的一个可以用来进行图像识别的数据集，里面包含了包括object detection，keypoints estimation, semantic segmentation，image caption等多个任务所需要的图像数据及其标注信息。以MS COCO 2017为例，一共 18G 左右的图片（训练集+验证集）和 0.75G 左右的 annotation 文件，annotation 文件的格式为 .json 格式，存储了所有图片中的目标类别 (总共 80 类)，目标 bounding box 的精确坐标以及目标的分割轮廓信息等。

The COCO dataset is a large-scale benchmark dataset with the full name of Common Objects in Context. It was developed by the Microsoft team for image recognition tasks, and contains image data and corresponding annotation information required for multiple downstream tasks including object detection, keypoints estimation, semantic segmentation, image captioning, and so on. Taking MS COCO 2017 as an example, it includes approximately 18 gigabytes of images (training set + validation set) and around 0.75 gigabytes of annotation files. These annotation files are in .json format, which store the target categories (total 80 classes) of all images, the precise coordinates of target bounding boxes, as well as the segmentation contour information of the targets and other relevant details.

搜集汇总

背景与挑战

背景概述

MSCOCO2017数据集是微软团队提供的大型图像识别数据集，主要用于目标检测、关键点估计、语义分割和图像描述等计算机视觉任务。该数据集包含约18G的图片和0.75G的标注文件，标注以.json格式存储，涵盖80个目标类别、精确的边界框坐标和分割轮廓信息，适用于多任务模型训练和评估。