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User:Datakeeper/DatasetsOnDeck

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

All of the following datasets have been added to List of datasets for machine learning research

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Dataset Name Brief description Preprocessing Instances Format Default Task Created (updated) Reference Creator
PASCAL VOC Dataset Large number of images for classification tasks. Labeling, bounding box included 500,000 Images, text Classification, object detection 2010 [1][2] M. Everingham et al.
UCF 101 Dataset 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 Dataset 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 Dataset Many small, low-resolution, images of 10 classes of objects. Classes labelled, training set splits created. 60,000 Images Classification 2009 [9][10] A. Krizhevsky et al.
CIFAR-100 Dataset 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.
Caltech-UCSD Birds-200-2011 Dataset Large dataset of images of birds. Part locations for birds, bounding boxes, 312 binary attributes given 11,788 Images, text Classification 2011 [11][12] C. Wah et al.
Oxford Flower Dataset 17 category dataset of flowers. Train/test splits, labeled images, 1360 Images, text Classification 2006 [13][14] M-E Nilsback et al.
KITTI Vision Benchmark Dataset Autonomous vehicles driving through a mid-size city captured images of various areas using cameras and laser scanners. Many benchmarks extracted from data. >100 GB of data Images, text Classification, object detection 2012 [15][16] A Geiger et al.

References

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  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.
  11. ^ Wah, Catherine, et al. "The caltech-ucsd birds-200-2011 dataset." (2011).
  12. ^ Duan, Kun, et al. "Discovering localized attributes for fine-grained recognition." Computer Vision and Pattern Recognition (CVPR), 2012 IEEE Conference on. IEEE, 2012.
  13. ^ Nilsback, Maria-Elena, and Andrew Zisserman. "A visual vocabulary for flower classification."Computer Vision and Pattern Recognition, 2006 IEEE Computer Society Conference on. Vol. 2. IEEE, 2006.
  14. ^ Razavian, Ali, et al. "CNN features off-the-shelf: an astounding baseline for recognition." Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition Workshops. 2014.
  15. ^ Geiger, Andreas, Philip Lenz, and Raquel Urtasun. "Are we ready for autonomous driving? the kitti vision benchmark suite." Computer Vision and Pattern Recognition (CVPR), 2012 IEEE Conference on. IEEE, 2012.
  16. ^ Sturm, Jürgen, et al. "A benchmark for the evaluation of RGB-D SLAM systems." Intelligent Robots and Systems (IROS), 2012 IEEE/RSJ International Conference on. IEEE, 2012.
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