Jump to content

User:Datakeeper/DatasetsOnDeck

From Wikipedia, the free encyclopedia
This is an old revision of this page, as edited by Datakeeper (talk | contribs) at 21:18, 2 May 2016 (CIFAR). The present address (URL) is a permanent link to this revision, which may differ significantly from the current revision.

The purpose of this page is to curate datasets before putting them on pages like List of datasets for machine learning research.

Dataset Name Brief description Preprocessing Instances Format Default Task Created (updated) Reference Creator
PASCAL VOC Large number of images for classification tasks. Labeling, bounding box included 500,000 Images, text Classificaiton, object detection 2010 [1][2] M. Everingham et al.
UCF 101 Self described as "a dataset of 101 human actions classes from videos in the wild." Dataset is large with over 27 hours of video. Actions classified and labeled. 13,000 Video, images, text Classification, action detection 2012 [3][4] K. Soomro et al.
THUMOS Large video dataset for action classification. Actions classified and labeled. 45M frames of video Video, images, text Classification, action detection 2013 [5][6] Y. Jiang et al.
German Traffic Sign Detection Benchmark Dataset Images from vehicles of traffic signs on German roads. These signs comply with UN standards and therefore are the same as in other countries. Signs manually labeled 900 Images Classification 2013 [7][8] S Houben et al.
CIFAR-10 Many small, low-resolution, images of 10 classes of objects. Classes labelled, training set splits created. 60,000 Images Classificaiton 2009 [9][10] A. Krizhevsky et al.
CIFAR-100 Like CIFAR-10, above, but 100 classes of objects are given. Classes labelled, training set splits created. 60,000 Images Classification 2009 [9][10] A. Krizhevsky et al.

References

  1. ^ Everingham, Mark, et al. "The pascal visual object classes (voc) challenge."International journal of computer vision 88.2 (2010): 303-338.
  2. ^ Felzenszwalb, Pedro F., et al. "Object detection with discriminatively trained part-based models." Pattern Analysis and Machine Intelligence, IEEE Transactions on 32.9 (2010): 1627-1645.
  3. ^ Soomro, Khurram, Amir Roshan Zamir, and Mubarak Shah. "UCF101: A dataset of 101 human actions classes from videos in the wild." arXiv preprint arXiv:1212.0402 (2012).
  4. ^ Karpathy, Andrej, et al. "Large-scale video classification with convolutional neural networks." Proceedings of the IEEE conference on Computer Vision and Pattern Recognition. 2014.
  5. ^ Jiang, Y. G., et al. "THUMOS challenge: Action recognition with a large number of classes." ICCV Workshop on Action Recognition with a Large Number of Classes, http://crcv. ucf. edu/ICCV13-Action-Workshop. 2013.
  6. ^ Simonyan, Karen, and Andrew Zisserman. "Two-stream convolutional networks for action recognition in videos." Advances in Neural Information Processing Systems. 2014.
  7. ^ Houben, Sebastian, et al. "Detection of traffic signs in real-world images: The German Traffic Sign Detection Benchmark." Neural Networks (IJCNN), The 2013 International Joint Conference on. IEEE, 2013.
  8. ^ Mathias, Mayeul, et al. "Traffic sign recognition—How far are we from the solution?." Neural Networks (IJCNN), The 2013 International Joint Conference on. IEEE, 2013.
  9. ^ a b Krizhevsky, Alex, Ilya Sutskever, and Geoffrey E. Hinton. "Imagenet classification with deep convolutional neural networks." Advances in neural information processing systems. 2012.
  10. ^ a b Gong, Yunchao, and Svetlana Lazebnik. "Iterative quantization: A procrustean approach to learning binary codes." Computer Vision and Pattern Recognition (CVPR), 2011 IEEE Conference on. IEEE, 2011.