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+ # ObjectNet (Test set only)
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+ Original paper: [ObjectNet: A large-scale bias-controlled dataset for pushing the limits of object recognition models](https://objectnet.dev/objectnet-a-large-scale-bias-controlled-dataset-for-pushing-the-limits-of-object-recognition-models.pdf)
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+ Homepage: https://objectnet.dev/
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+ Bibtex:
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+ ```
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+ @inproceedings{NEURIPS2019_97af07a1,
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+ author = {Barbu, Andrei and Mayo, David and Alverio, Julian and Luo, William and Wang, Christopher and Gutfreund, Dan and Tenenbaum, Josh and Katz, Boris},
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+ booktitle = {Advances in Neural Information Processing Systems},
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+ editor = {H. Wallach and H. Larochelle and A. Beygelzimer and F. d\textquotesingle Alch\'{e}-Buc and E. Fox and R. Garnett},
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+ pages = {},
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+ publisher = {Curran Associates, Inc.},
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+ title = {ObjectNet: A large-scale bias-controlled dataset for pushing the limits of object recognition models},
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+ url = {https://proceedings.neurips.cc/paper/2019/file/97af07a14cacba681feacf3012730892-Paper.pdf},
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+ volume = {32},
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+ year = {2019}
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+ }
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+ ```