hjgw / efficientnetv2.pytorch

PyTorch implementation of EfficientNetV2 family

Home Page:https://arxiv.org/abs/2104.00298

Geek Repo:Geek Repo

Github PK Tool:Github PK Tool

[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.


Architecture # Parameters FLOPs Top-1 Acc. (%)
EfficientNetV2-S 22.10M 8.42G @ 384
EfficientNetV2-M 55.30M 24.74G @ 480
EfficientNetV2-L 119.36M 56.13G @ 480
EfficientNetV2-XL 208.96M 93.41G @ 512

Stay tuned for ImageNet pre-trained weights.


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

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}
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}

The official TensorFlow implementation by @mingxingtan.


PyTorch implementation of EfficientNetV2 family


License:MIT License


Language:Python 100.0%