https://github.com/d-li14/efficientnetv2.pytorch
PyTorch implementation of EfficientNetV2 family
https://github.com/d-li14/efficientnetv2.pytorch
efficientnet efficientnetv2 icml2021 imagenet pytorch-implementation
Last synced: 6 months ago
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PyTorch implementation of EfficientNetV2 family
- Host: GitHub
- URL: https://github.com/d-li14/efficientnetv2.pytorch
- Owner: d-li14
- License: mit
- Created: 2021-04-02T13:05:58.000Z (over 4 years ago)
- Default Branch: main
- Last Pushed: 2022-02-20T08:57:32.000Z (over 3 years ago)
- Last Synced: 2025-03-29T15:11:24.168Z (6 months ago)
- Topics: efficientnet, efficientnetv2, icml2021, imagenet, pytorch-implementation
- Language: Python
- Homepage: https://arxiv.org/abs/2104.00298
- Size: 20.5 KB
- Stars: 467
- Watchers: 10
- Forks: 95
- Open Issues: 7
-
Metadata Files:
- Readme: README.md
- License: LICENSE
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README
**[NEW!]** Check out our latest work [involution](https://github.com/d-li14/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](https://arxiv.org/abs/2104.00298) by Mingxing Tan, Quoc V. Le with the [PyTorch](pytorch.org) framework.
## Models
| 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.
## Acknowledgement
The implementation is heavily borrowed from [HBONet](https://github.com/d-li14/HBONet) or [MobileNetV2](https://github.com/d-li14/mobilenetv2.pytorch), 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}
}
```
The official [TensorFlow implementation](https://github.com/google/automl/tree/master/efficientnetv2) by [@mingxingtan](https://github.com/mingxingtan).