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VOC2012 Detection
2D Box
Common
|...
License: Custom

Overview

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

  1. Classification: For each of the twenty classes, predicting presence/absence of an
    example of that class in the test image.
  2. Detection: Predicting the bounding box and
    label of each object from the twenty target classes in the test image.

Participants may enter
either (or both) of these competitions, and can choose to tackle any (or all) of the twenty
object classes. The challenge allows for two approaches to each of the competitions:

  1. Participants
    may use systems built or trained using any methods or data excluding the provided test sets.
  2. Systems are to be built or trained using only the provided training/validation data.

The intention in the first case is to establish just what level of success can currently be
achieved on these problems and by what method; in the second case the intention is to establish
which method is most successful given a specified training set.

Data Preview

Label Distribution

Citation

Please use the following citation when referencing the dataset:

@misc{pascal-voc-2012,
author = "Everingham, M. and Van~Gool, L. and Williams, C. K. I. and Winn, J. and Zisserman, A.",
title = "The {PASCAL} {V}isual {O}bject {C}lasses {C}hallenge 2012 {(VOC2012)} {R}esults",
howpublished = "http://www.pascal-network.org/challenges/VOC/voc2012/workshop/index.html"}

License

Custom

Data Summary
Type
Image,
Amount
17.125K
Size
1.86GB
Provided by
The PASCAL Visual Object Classes.
Pattern Analysis, Statistical Modelling and Computational learning.
| Amount 17.125K | Size 1.86GB
VOC2012 Detection
2D Box
Common
License: Custom

Overview

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

  1. Classification: For each of the twenty classes, predicting presence/absence of an
    example of that class in the test image.
  2. Detection: Predicting the bounding box and
    label of each object from the twenty target classes in the test image.

Participants may enter
either (or both) of these competitions, and can choose to tackle any (or all) of the twenty
object classes. The challenge allows for two approaches to each of the competitions:

  1. Participants
    may use systems built or trained using any methods or data excluding the provided test sets.
  2. Systems are to be built or trained using only the provided training/validation data.

The intention in the first case is to establish just what level of success can currently be
achieved on these problems and by what method; in the second case the intention is to establish
which method is most successful given a specified training set.

Data Preview

Label Distribution

Citation

Please use the following citation when referencing the dataset:

@misc{pascal-voc-2012,
author = "Everingham, M. and Van~Gool, L. and Williams, C. K. I. and Winn, J. and Zisserman, A.",
title = "The {PASCAL} {V}isual {O}bject {C}lasses {C}hallenge 2012 {(VOC2012)} {R}esults",
howpublished = "http://www.pascal-network.org/challenges/VOC/voc2012/workshop/index.html"}

License

Custom

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