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