ImageNet

ImageNet is an image dataset organized according to the WordNet hierarchy. Each meaningful concept in WordNet, possibly described by multiple words or word phrases, is called a ”synonym set” or ”synset”. There are more than 100,000 synsets in WordNet, majority of them are nouns (80,000+). In ImageNet, we aim to provide on average 1000 images to illustrate each synset. Images of each concept are quality-controlled and human-annotated. In its completion, we hope ImageNet will offer tens of millions of cleanly sorted images for most of the concepts in the WordNet hierarchy.


References in zbMATH (referenced in 77 articles )

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  1. Fernando Pérez-García, Rachel Sparks, Sebastien Ourselin: TorchIO: a Python library for efficient loading, preprocessing, augmentation and patch-based sampling of medical images in deep learning (2020) arXiv
  2. Fung, Samy Wu; Tyrväinen, Sanna; Ruthotto, Lars; Haber, Eldad: ADMM-softmax: an ADMM approach for multinomial logistic regression (2020)
  3. Gahrooei, Mostafa Reisi; Yan, Hao; Paynabar, Kamran: Comments on: “On active learning methods for manifold data” (2020)
  4. Parmida Atighehchian, Frédéric Branchaud-Charron, Alexandre Lacoste: Bayesian active learning for production, a systematic study and a reusable library (2020) arXiv
  5. P.E. Hadjidoukas, A. Bartezzaghi, F. Scheidegger, R. Istrate, C.Bekas, A.C.I. Malossi: torcpy: Supporting task parallelism in Python (2020) not zbMATH
  6. Shen, Yexin; Cao, Jiuwen; Wang, Jianzhong; Yang, Zhixin: Urban acoustic classification based on deep feature transfer learning (2020)
  7. Xiao, Heng; Wu, Jin-Long; Laizet, Sylvain; Duan, Lian: Flows over periodic hills of parameterized geometries: a dataset for data-driven turbulence modeling from direct simulations (2020)
  8. Axel Barroso-Laguna, Edgar Riba, Daniel Ponsa, Krystian Mikolajczyk: Key.Net: Keypoint Detection by Handcrafted and Learned CNN Filters (2019) arXiv
  9. Cai, Hongmin; Huang, Qinjian; Rong, Wentao; Song, Yan; Li, Jiao; Wang, Jinhua; Chen, Jiazhou; Li, Li: Breast microcalcification diagnosis using deep convolutional neural network from digital mammograms (2019)
  10. Calder, Jeff: The game theoretic (p)-Laplacian and semi-supervised learning with few labels (2019)
  11. Dai, Xiaoliang; Yin, Hongxu; Jha, Niraj K.: NeST: a neural network synthesis tool based on a grow-and-prune paradigm (2019)
  12. Diaz-Chito, Katerine; Martínez del Rincón, Jesús; Rusiñol, Marçal; Hernández-Sabaté, Aura: Feature extraction by using dual-generalized discriminative common vectors (2019)
  13. Gao, Depeng; Liu, Jiafeng; Wu, Rui; Cheng, Dansong; Fan, Xiaopeng; Tang, Xianglong: Utilizing relevant RGB-d data to help recognize RGB images in the target domain (2019)
  14. He, Juncai; Xu, Jinchao: Mgnet: A unified framework of multigrid and convolutional neural network (2019)
  15. Higham, Catherine F.; Higham, Desmond J.: Deep learning: an introduction for applied mathematicians (2019)
  16. Huan, Er-Yang; Wen, Gui-Hua: Multilevel and multiscale feature aggregation in deep networks for facial constitution classification (2019)
  17. Ji, Qingge; Huang, Jie; He, Wenjie; Sun, Yankui: Optimized deep convolutional neural networks for identification of macular diseases from optical coherence tomography images (2019)
  18. Kaiyang Zhou, Tao Xiang: Torchreid: A Library for Deep Learning Person Re-Identification in Pytorch (2019) arXiv
  19. Liu, Yimin; Sun, Wenyue; Durlofsky, Louis J.: A deep-learning-based geological parameterization for history matching complex models (2019)
  20. Lüddecke, Timo; Agostini, Alejandro; Fauth, Michael; Tamosiunaite, Minija; Wörgötter, Florentin: Distributional semantics of objects in visual scenes in comparison to text (2019)

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Further publications can be found at: http://image-net.org/about-publication