README_FixEfficientNet.md

FixEfficientNet

FixRes is a simple method for fixing the train-test resolution discrepancy. It improves the performance of any convolutional neural network architecture. The method is described in the Neurips paper "Fixing the train-test resolution discrepancy" (More results on arXiv).

Hereafter we provide some results reported in this note for EfficientNet models. These models depend on and improve previous trained models, see the references to other models.

ImageNet Results

Models	Resolution	#Parameters	Top-1 / Top-5	Extra training data
FixEfficientNet-B0	320	5.3M	79.3 / 94.6
FixEfficientNet-B0	320	5.3M	80.2 / 95.4	x
FixEfficientNet-B1	384	7.8M	81.3 / 95.7
FixEfficientNet-B1	384	7.8M	82.6 / 96.4	x
FixEfficientNet-B2	420	9.2M	82.0 / 96.0
FixEfficientNet-B2	420	9.2M	83.6 / 96.9	x
FixEfficientNet-B3	472	12M	83.0 / 96.4
FixEfficientNet-B3	472	12M	85.0 / 97.4	x
FixEfficientNet-B4	512	19M	84.0 / 97.0
FixEfficientNet-B4	472	19M	85.9 / 97.7	x
FixEfficientNet-B5	576	30M	84.7 / 97.2
FixEfficientNet-B5	576	30M	86.4/ 97.9	x
FixEfficientNet-B6	576	43M	84.9 / 97.3
FixEfficientNet-B6	680	43M	86.7 / 98.0	x
FixEfficientNet-B7	632	66M	85.3 / 97.4
FixEfficientNet-B7	632	66M	87.1 / 98.2	x
FixEfficientNet-B8	800	87.4M	85.7 / 97.6
FixEfficientNet-L2	600	480M	88.5 / 98.7	x

@inproceedings{touvron2019FixRes,
       author = {Touvron, Hugo and Vedaldi, Andrea and Douze, Matthijs and J{\'e}gou, Herv{\'e}},
       title = {Fixing the train-test resolution discrepancy},
       booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
       year = {2019},
}

@misc{touvron2020FixEfficientNet,
       author = {Touvron, Hugo and Vedaldi, Andrea and Douze, Matthijs and J{\'e}gou, Herv{\'e}},
       title = {Fixing the train-test resolution discrepancy: FixEfficientNet},
       journal={arXiv preprint arXiv:2003.08237},
       year = {2020},
}

References to other models

Model definition scripts and pretrained weights are from https://github.com/rwightman/pytorch-image-models.

The corresponding papers are as follows.

For models with extra-training data:

@misc{xie2019selftraining,
    author={Qizhe Xie and Minh-Thang Luong and Eduard Hovy and Quoc V. Le,
    title="{Self-training with Noisy Student improves ImageNet classification}",
    journal = {arXiv preprint arXiv:1911.04252},
    year=2019,
}

For models without extra-training data:

@misc{xie2019adversarial,
    author={Cihang Xie and Mingxing Tan and Boqing Gong and Jiang Wang and Alan Yuille and Quoc V. Le,
    title="{Adversarial Examples Improve Image Recognition}",
    journal = {arXiv preprint arXiv:1911.09665},
    year="2019",
}

@misc{tan2019efficientnet,
  author    = {Mingxing Tan and Quoc V. Le},
  title     = "{EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks}",
  journal   = {arXiv preprint arXiv:1905.11946},
  year= "2019",
}

License

FixRes is CC BY-NC 4.0 licensed, as found in the LICENSE file.