Labelme

Labelme数据集是用于对象识别的图像数据集，涵盖了1000多个完全注释和2000部分注释的图像，其中一些可用于训练标记算法，测试集具有从世界不同地区拍摄的图像。这确保了图像在继续和测试之间会有很大的差异。
该数据集由麻省理工学院计算机科学和人工智能实验室2007年发布。相关论文是 “LabelMe: 用于图像注释的数据库和基于web的工具”，该项目存在相关挑战。

LabelMe Dataset is an image dataset for object recognition, containing over 1000 fully annotated and 2000 partially annotated images. A portion of these images can be utilized for training annotation algorithms, while the test set comprises images captured across various regions globally. This setup ensures a significant distribution discrepancy between the training and test datasets.
This dataset was released in 2007 by the MIT Computer Science and Artificial Intelligence Laboratory (CSAIL). Its associated paper is titled "LabelMe: A Database and Web-Based Tool for Image Annotation", and related challenge tasks are available for this project.

搜集汇总

背景与挑战

背景概述

Labelme数据集是一个2007年发布的图像标注数据集，包含3000多张部分或完全标注的图像，主要用于对象识别算法的训练和测试，具有地理多样性特点。