PyTorch implementation of EfficientNetV2

Related tags

Deep Learning pytorch-implementation efficientnet
Overview

[NEW!] Check out our latest work involution accepted to CVPR'21 that introduces a new neural operator, other than convolution and self-attention.

PyTorch implementation of EfficientNet V2

Reproduction of EfficientNet V2 architecture as described in EfficientNetV2: Smaller Models and Faster Training by Mingxing Tan, Quoc V. Le with the PyTorch framework.

Requirements

PyTorch 1.7+ is required to support nn.SiLU

Models

Architecture # Parameters FLOPs Top-1 Acc. (%)
EfficientNetV2-S 24M 8.8B

More model definitions are pending for architectural details from the authors.

Stay tuned for ImageNet pre-trained weights.

Acknowledgement

The implementation is heavily borrowed from HBONet or MobileNetV2, please kindly consider citing the following

@InProceedings{Li_2019_ICCV,
author = {Li, Duo and Zhou, Aojun and Yao, Anbang},
title = {HBONet: Harmonious Bottleneck on Two Orthogonal Dimensions},
booktitle = {The IEEE International Conference on Computer Vision (ICCV)},
month = {Oct},
year = {2019}
}

@InProceedings{Sandler_2018_CVPR,
author = {Sandler, Mark and Howard, Andrew and Zhu, Menglong and Zhmoginov, Andrey and Chen, Liang-Chieh},
title = {MobileNetV2: Inverted Residuals and Linear Bottlenecks},
booktitle = {The IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},
month = {June},
year = {2018}
}
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Comments
  • About SE layer in Fused-MBConv

    About SE layer in Fused-MBConv

    截屏2021-04-21 下午3 32 37 I understand that the SE layer might get removed in the original fusion attempt: https://ai.googleblog.com/2019/08/efficientnet-edgetpu-creating.html

    But according to Fig2 in the paper, it seems like the SE layer is preserved in the Fused MBConv. So I am quite confused when I found the SE layer lacking in your implementation. Have you ever tried the implementation with SE layer, or am I missing something in the paper?

    opened by Noir97 4
  • Error of CIFAR10 Dataset

    Error of CIFAR10 Dataset

    The code cannot run on the CIFAR10 dataset because the image size of CIFAR10 is 32*32 and an error will be reported if the input dimension is (1, 3, 32, 32)

    opened by ZhengHui-Z 2
  • Syncing with the implementation of official repo

    Syncing with the implementation of official repo

    The official repo made some changes that are different from the paper. For example, the paper claims that v2-s uses 272 channels in the last stage, but they changed to 256 in their code, and the author claimed that the code will be the source of truth. Is this repo planning on syncing with the official code? Thanks

    opened by seermer 1
Owner
Duo Li
Duo Li
GitHub https://arxiv.org/abs/2104.00298
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