The PASCAL Object Recognition Database Collection


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  1. Fully Annotated Databases
    1. The TU Darmstadt Database (formerly the ETHZ Database)
    2. The UIUC Image Database for Car Detection
    3. The VOC2005 Database: Dataset 1
    4. The VOC2005 Database: Testset 2
    5. The VOC2006 Database
  2. Partially Annotated Databases
    1. The Caltech Database
    2. The MIT-CSAIL Database of Objects and Scenes
    3. The TU Graz-02 Database
  3. Unannotated Databases
    1. 101 Object Categories

Fully Annotated Databases

1.1   The TU Darmstadt Database (formerly the ETHZ Database)

Authors Bastian Leibe and Bernt Schiele
Institute Darmstadt University of Technology
Database URL http://www.mis.informatik.tu-darmstadt.de/leibe
PASCAL download Download tar.gz file of annotated PNG images
Categories Side views of motorbikes, cars and cows
Number of images 115 motorbikes + 50 x 2 cars + 112 cows = 327
Number of annotated images 326 (cow-pic530-sml-lt discarded because of incorrect segmentation mask)
Object annotation statistics 125 PASmotorbikeSide objects + 100 PAScarSide objects + 111 PAScowSide objects
Annotation notes The original ground truth data provided by the authors is given in terms of bounding boxes for the motorbikes and pixel segmentation masks for the cows and the cars
The original segmentation masks, which are non-binary RGB triplets, have been converted to PASCAL masks (0=background, 1=object) by converting the colour masks to grey scale values between 0 and 255 and then thresholding at 128
Browse Browse all images
Acknowledgements Cow data provided by Derek Magee, University of Leeds.
This work has been part of the CogVis project, funded in part by the Commission of the European Union (IST-2000-29375), and the Swiss Federal Office for Education and Science (BBW 00.0617)
Publications B. Leibe, A. Leonardis and B. Schiele. Combined object categorization and segmentation with an implicit shape model. In Proceedings of the Workshop on Statistical Learning in Computer Vision. Prague, Czech Republic, May 2004.
Bibtex source | Download in pdf format
D. Magee and R. Boyle. Detecting Lameness using "Re-sampling Condensation" and "Multi-stream Cyclic Hidden Markov Models." Image and Vision Computing, vol 20(8), pp 581-594, 2002.

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Database variability and level of difficulty for objection recognition:


1.2   The UIUC Image Database for Car Detection

Authors Shivani Agarwal, Aatif Awan and Dan Roth
Institute University of Illinois at Urbana-Champaign
Database URL http://l2r.cs.uiuc.edu/~cogcomp/Data/Car/
PASCAL download Download tar.gz file of annotated PNG images
Categories Side views of cars, negative examples (images without cars)
Number of images 550 training cars + 170 test cars + 108 multi-scale test cars + 500 negative training examples = 1328
Number of annotated images 1328
Object annotation statistics 889 PAScarSide objects + 500 PASbackground objects
Annotation notes The original ground truth data provided by the authors is given in terms of bounding boxes for the cars
All cars have been assigned the label PAScarSide
Some test images have multiple cars
Following the original annotations, bounding boxes which lie outside the image have not been clipped
All negative examples have been assigned the label PASbackground (with the original label field being set to "Not carSide") and their bounding boxes have been set to the image dimensions
Browse Browse all images
Acknowledgements This research, including the collection of this database, was supported by NSF grants ITR IIS 00-85980 and ITR IIS 00-85836
Publications S. Agarwal and D. Roth. Learning a sparse representation for object detection. In Proceedings of the European Conference on Computer Vision, volume 4, pages 113--130. Copenhagen, Denmark, May 2002. Springer-Verlag.
Bibtex source | Download in pdf format
S. Agarwal, A. Awan and D. Roth. Learning to detect objects in images via a sparse, part-based representation. IEEE Transactions on Pattern Analysis and Machine Intelligence, 26(11):1475--1490, November 2004.
Bibtex source | Download in pdf format

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Database variability and level of difficulty for objection recognition:


1.3   The VOC 2005 Database: Dataset 1

Authors Mark Everingham  (Compiled by)
Institute University of Oxford  
Database URL http://www.pascal-network.org/challenges/VOC/databases.html
PASCAL download Download tar.gz file of annotated PNG images
Categories Views of motorbikes, bicycles, people, and cars in arbitrary pose.
Number of images 1578 
Number of annotated images 1578 
Object annotation statistics Total number of labelled objects =  2209
Annotation notes The images in this database are a subset of the other image databases on this page. The images were manually selected as an "easier" dataset for the 2005 VOC challenge. Annotations were taken verbatim from the source databases.
Browse Browse all images
Acknowledgements Images in this database were taken from the TU-Darmstadt, Caltech, TU-Graz and UIUC databases. Additional images were provided by INRIA. Funding was provided by PASCAL.
Publications M. Everingham, A. Zisserman, C. K. I. Williams, L. Van Gool, et al. The 2005 PASCAL Visual Object Classes Challenge. In Machine Learning Challenges. Evaluating Predictive Uncertainty, Visual Object Classification, and Recognising Textual Entailment., eds. J. Quinonero-Candela, I. Dagan, B. Magnini, and F. d'Alche-Buc, LNAI 3944, pages 117-176, Springer-Verlag, 2006.
Download in pdf format

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1.3   The VOC 2005 Database: Testset 2

Authors Mark Everingham  
Institute University of Oxford  
Database URL http://www.pascal-network.org/challenges/VOC/databases.html
PASCAL download Download tar.gz file of annotated PNG images
Categories Views of motorbikes, bicycles, people, and cars in arbitrary pose.
Number of images 654 
Number of annotated images 654 
Object annotation statistics Total number of labelled objects = 1293
Annotation notes These images were collected from Google for the 2005 VOC challenge. The images were chosen to provide a "harder" test set for the challenge. All images are annotated with instances of all four categories: motorbikes, bicycles, people and cars.
Browse Browse all images
Acknowledgements Funding was provided by PASCAL.
Publications M. Everingham, A. Zisserman, C. K. I. Williams, L. Van Gool, et al. The 2005 PASCAL Visual Object Classes Challenge. In Machine Learning Challenges. Evaluating Predictive Uncertainty, Visual Object Classification, and Recognising Textual Entailment., eds. J. Quinonero-Candela, I. Dagan, B. Magnini, and F. d'Alche-Buc, LNAI 3944, pages 117-176, Springer-Verlag, 2006.
Download in pdf format

Click on images to see annotations.


1.4   The VOC 2006 Database

Authors Mark Everingham  (Compiled by)
Institute University of Oxford  
Database URL http://www.pascal-network.org/challenges/VOC/databases.html
PASCAL download Download tar file of annotated PNG images: train+val sets
Download tar file of annotated PNG images: test set
License By downloading the test data you are agreeing to abide by the licenses for the "flickr" and Microsoft Research Cambridge images contained in the database:
"flickr" terms of use
MSR Cambridge License (RTF)
Details of the contributor of each image can be found in the file "contrib.txt" included in the database.
Categories Views of bicycles, buses, cats, cars, cows, dogs, horses, motorbikes, people, sheep in arbitrary pose.
Number of images 5,304
Number of annotated images 5,304
Object annotation statistics Total number of labelled objects = 9,507
Annotation notes These images were collected from personal photographs, "flickr", and the Microsoft Research Cambridge database for the 2006 VOC challenge. All images are annotated with instances of all ten categories: bicycles, buses, cats, cars, cows, dogs, horses, motorbikes, people, sheep. Guidelines used for the annotation are available here.
Browse Browse all images
Acknowledgements Funding was provided by PASCAL.
Images were contributed and/or annotated by Moray Allen, James Bednar, Matthijs Douze, Mark Everingham, Stefan Harmeling, Juan Huo, Lindsay Hutchison, Fiona Jamieson, Maria-Elena Nilsback, John Quinn, Florian Schroff, Kira Smyllie, Mark Van Rossum, Chris Williams, John Winn, Andrew Zisserman.
Publications
Bibtex source | Download in pdf format
M. Everingham, A. Zisserman, C. K. I. Williams, L. Van Gool. The 2006 PASCAL Visual Object Classes Challenge (VOC2006) Results.
http://www.pascal-network.org/challenges/VOC/voc2006/results.pdf.

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Partially Annotated Databases

2.1   The Caltech Database

Authors Rob Fergus and Pietro Perona
Institute California Institute of Technology
Database URLs http://www.robots.ox.ac.uk/~vgg/data3.html
http://www.vision.caltech.edu/html-files/archive.html
PASCAL download Download tar.gz file of annotated PNG images
Categories Profile views of aeroplanes and motorbikes, rear views of cars, car backgrounds, front views of faces and general background scenes
Number of images 1074 aeroplanes + 1155 cars + 450 faces + 826 motorbikes + 1370 car backgrounds + 900 general backgrounds = 5775
Number of annotated images 4620
Object annotation statistics Total number of labelled objects = 1293
Annotation notes The original ground truth data provided by the authors is given in terms of a bounding quadrilateral which is converted into a bounding rectangle
Following the original annotations, in some cases only one object per image is labelled even though there are multiple instances present.
Browse Browse images selected for annotation
Acknowledgements Funding was provided by the UK EPSRC; Caltech Center for Neuromorphic Systems Engineering and EC CogViSys project.
Michalis Titsias annotated the car images.
Publications R. Fergus, P. Perona and A. Zisserman. Object Class recognition by unsupervised scale-invariant learning. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, volume 2, pages 264--271. Madison, Wisconsin, June 2003.
Bibtex source | Download in pdf format

Click on images to see annotations.

Database variability and level of difficulty for objection recognition:


2.2   The MIT-CSAIL Database of Objects and Scenes

Authors Antonio Torralba, Kevin P. Murphy and William T. Freeman
Institute Massachusetts Institute of Technology
Database URL http://web.mit.edu/torralba/www/database.html
PASCAL download Download tar.gz file of annotated PNG images: part 1, part 2
Categories 107 object classes and 18 region classes
Number of images Over 72,000
Number of annotated images 2873
Object annotation statistics Total number of labelled objects = 10,358.
View number of objects in each class
Annotation notes The original ground truth data provided by the authors is given in terms of boundary polygons for each labelled object
Some very minor errors in the original annotations relating to image sizes and colour depth have been corrected
All the original object labels have been retained. Their PASCAL equivalents are obtained by adding a PAS in front of them
180 of the original annotated images which did not contain any objects have been assigned the label PASbackground
Browse Browse, by subdirectory, images selected for annotation
Browse, by object class, images selected for annotation
Acknowledgements Egon Pasztor made many contribution in the early stages of the database. We also want to give thanks to the flight delays and specially to the bad television programs who motivated us very much into annotating more images every day
Publications A. Torralba, K. P. Murphy and W. T. Freeman. Sharing features: efficient boosting procedures for multiclass object detection. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, volume 2, pages 762--769. Washington, DC, June 2004.
Bibtex source | Download in pdf format

Click on images to see annotations.

Database variability and level of difficulty for objection recognition:


2.3   The TU Graz-02 Database

Authors Andreas Opelt and Axel Pinz
Institute Graz University of Technology
Database URL http://www.emt.tugraz.at/~pinz/data/GRAZ_02/
PASCAL download Download tar.gz file of annotated PNG images
Categories Bikes, cars, people and backgrounds
Number of images 365 bikes + 420 cars + 311 people + 380 negative examples = 1476
Number of annotated images 1280
Object annotation statistics 404 PASbicycle objects + 451 PAScar + 581 PASperson objects + 380 PASbackground objects
Annotation notes The original ground truth data provided by the authors is given in terms of pixel segmentation masks
The original masks have values between 0 and 255. These are converted to PASCAL masks (0 = background, 1 = 1st object, 2 = 2nd object, etc.) by (a) setting all values greater than 0 as background, (b) finding the connected components of the foreground and (c) rejecting all components smaller than 100 pixels.
Following the original annotations, overlapping objects have been merged into a single object while disconnected objects parts have been labelled as separate objects.
The original negative examples had been labelled as "none". These are now labelled as PASbackground with the bounding box set to the dimensions of the image.
Browse Browse images selected for annotation
Acknowledgements The EU project LAVA (IST-2001-34405) and the Austrian Science Foundation (project S9103-N04)
Publications A. Opelt, M. Fussenegger, A. Pinz and P. Auer. Generic object recognition with boosting. Technical Report TR-EMT-2004-01, EMT, TU Graz, Austria, 2004. Submitted to the IEEE Transactions on Pattern Analysis and Machine Intelligence.
Bibtex source | Download in pdf format
A. Opelt and A. Pinz. Object localization with boosting and weak supervision for generic object recognition. In Proceedings of the 14th Scandinavian Conference on Image Analysis (SCIA).
Bibtex source | Download in pdf format

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Database variability and level of difficulty for objection recognition:


Unannotated Databases

3.1   101 Object Categories

Authors Fei-Fei Li, Marco Andreetto and Marc'Aurelio Ranzato
Institute California Institute of Technology
Database URL http://www.vision.caltech.edu/feifeili/101_ObjectCategories/
PASCAL download Download tar.gz file of unannotated PNG images
Categories 101 object classes
Number of images 9197
Browse Browse unannotated images from the database
Publications L. Fei-Fei, R. Fergus, and P. Perona. Learning generative visual models from few training examples: an incremental Bayesian approach tested on 101 object categories. In Proceedings of the Workshop on Generative-Model Based Vision. Washington, DC, June 2004.
Bibtex source | Download in pdf format

Some sample images from the 101 Object Categories database.