Visual Object Classes Challenge 2012 Dataset (VOC2012) VOCtrainval_11-May-2012.tar

##Introduction The main goal of this challenge is to recognize objects from a number of visual object classes in realistic scenes (i.e. not pre-segmented objects). It is fundamentally a supervised learning learning problem in that a training set of labelled images is provided. The twenty object classes that have been selected are: * Person: person * Animal: bird, cat, cow, dog, horse, sheep * Vehicle: aeroplane, bicycle, boat, bus, car, motorbike, train * Indoor: bottle, chair, dining table, potted plant, sofa, tv/monitor There are three main object recognition competitions: classification, detection, and segmentation, a competition on action classification, and a competition on large scale recognition run by ImageNet. In addition there is a "taster" competition on person layout. ##Classification/Detection Competitions Classification: For each of the twenty classes, predicting presence/absence of an example of that class in the test image. Detection: Predicting the bounding b

本挑战的主要目标是识别现实场景中（即非预先分割对象）的多种视觉物体类别。从根本上讲，这是一个监督学习问题，因为提供了一个标注图像的训练集。所选的二十种物体类别包括：* 人类：人类 * 动物：鸟类、猫、牛、狗、马、羊 * 交通工具：飞机、自行车、船只、公共汽车、汽车、摩托车、火车 * 室内物品：瓶子、椅子、餐桌、盆栽植物、沙发、电视/显示器。存在三个主要的目标识别竞赛：分类、检测和分割，以及由ImageNet运行的规模识别竞赛。此外，还设有关于人物布局的“试水”竞赛。##分类/检测竞赛 分类：对于每个类别，预测测试图像中该类别实例的存在/不存在。检测：预测边界框。