LFW: Labeled Faces in the Wild

By Amirarsalan Rajabi | November 9, 2018 | Comments Off on LFW: Labeled Faces in the Wild

LFW: Labeled Faces in the Wild:

This dataset contains labeled face images collected from the web with names of the people in the images as the labels. Some of these people have two or more number of images in the dataset. This dataset is designed for studying the problem of unconstrained face recognition and face verification. The original LFW dataset is available for download along with 3 sets of aligned images (funneled images, LFW-a, deep funneled).

Here is some information regarding this dataset:

  • Number of images in the dataset: 13,000 images (10-fold cross validation is recommended and training and test splits can be downloaded from the dataset page)

  • Number of identities: 5749

  • Image resolution: 250×250

More details and links for download can be found on the dataset page http://vis-www.cs.umass.edu/lfw/.

If you use the any of these versions of the LFW image dataset:

Please make sure to cite the paper:

G. B. Huang, M. Ramesh, T. Berg, E. Learned-Miller, Labeled Faces in the Wild: A Database for Studying Face Recognition in Unconstrained Environments. University of Massachusetts, Amherst, Technical Report 07-49, October 2007.

If you use the LFW imaged aligned by deep funneling:

Please make sure to cite the paper:

G. B. Huang, M. Matter, H. Lee, E. Learned-Miller, Learning to Align from Scratch. Advances in Neural Information Processing Systems (NIPS), 2012.

If you use the LFW imaged aligned by funneling:

Please make sure to cite the paper:

G. B. Huang, V. Jain, E. Learned-Miller, Unsupervised Joint Alignment of Complex Images. International Conference on Computer Vision (ICCV), 2007.

keywords: Vision, Image, Face, Object Detection, Segmentation, In the Wild

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COCO

By Amirarsalan Rajabi | November 9, 2018 | Comments Off on COCO

COCO:

This image dataset contains image data suitable for object detection and segmentation. It contains 5 annotation types for Object Detection, Keypoint Detection, Stuff Segmentation, Panoptic Segmentation and Image Captioning all explained in details on the data format section of the dataset page (http://cocodataset.org/#format-data).

Here is some information regarding the latest version of this dataset:

  • Number of images in the dataset: 330,000 images while more than 200,000 are labeled (roughly equal halves for training and validation+test)

  • Number of classes: 80 object categories, 91 stuff categories

  • Image resolution: 640×480

More details and links for download can be found on the dataset and challenge page http://cocodataset.org/#home and http://cocodataset.org/#overview.

If you use this dataset:

Please make sure to read Terms of Use available on http://cocodataset.org/#termsofuse.

Please make sure to cite the paper:

T. Lin, M. Maire, S. Belongie, L. Bourdev, R. Girshick, J. Hays, P. Perona, D. Ramanan, C. Zitnick, P, Microsoft COCO: Common Objects in Context. Dollar. European Conference on Computer Vision (ECCV), 2014.

keywords: Vision, Image, Object Detection, Segmentation

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