PASCAL Visual Object Classes Challenge 2011 (VOC2011) Complete Dataset

Introduction The 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 Data To download the training/validation data, see the development kit. The training data provided consists of a set of images; each image has an annotation file giving a bounding box and object class label for each object in one of the twenty classes present in the image. Note that multiple objects from multiple classes may be present in the same image. Some example images can be viewed online. A subset of images are also annotated with pixel-wise segmentation of each object presen

引言 本挑战旨在识别真实场景中（即非预先分割对象）的多种视觉物体类别。本质上，这是一个监督学习问题，因为提供了标注图像的训练集。所选的二十种物体类别如下：
人物：人
动物：鸟、猫、牛、狗、马、羊
车辆：飞机、自行车、船、公共汽车、汽车、摩托车、火车
室内：瓶子、椅子、餐桌、盆栽、沙发、电视/显示器
数据 下载训练/验证数据，请参阅开发包。提供的训练数据包括一系列图像；每张图像都有一个标注文件，为图像中出现的二十个类别之一的每个对象提供边界框和类别标签。请注意，同一图像中可能包含来自多个类别的多个对象。一些示例图像可在网上查看。图像的一个子集还进行了每个对象的像素级分割的标注。