Remarks: The provided pretrained models are obtained using the latest refactored code and the performance are slightly different from the results in the paper.
E.g.,
$ cd AOGNets
$ ./examples/test_fp16.sh aognet_s AOGNet_12M_PATH
Citations
Please consider citing the AOGNets paper in your publications if it helps your research.
@inproceedings{AOGNets,
author = {Xilai Li and Xi Song and Tianfu Wu},
title = {AOGNets: Compositional Grammatical Architectures for Deep Learning},
booktitle = {{IEEE} Conference on Computer Vision and Pattern Recognition, {CVPR}},
year = {2019},
url = {https://arxiv.org/pdf/1711.05847.pdf}
}
Contact
Please feel free to report issues and any related problems to Xilai Li (xli47 at ncsu dot edu), Xi Song (xsong.lhi at gmail.com) and Tianfu Wu (twu19 at ncsu dot edu).