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[NeurIPS 2021] [T-PAMI] Global Filter Networks for Image Classification
| Date | Stars |
|---|---|
| 2026-07-24 | 511 |
| 2026-07-25 | 511 |
| 2026-07-28 | 511 |
| 2026-07-30 | 511 |
| 2026-08-06 | 511 |
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# Global Filter Networks for Image Classification
Created by [Yongming Rao](https://raoyongming.github.io/), [Wenliang Zhao](https://wl-zhao.github.io/), [Zheng Zhu](http://www.zhengzhu.net/), [Jiwen Lu](https://scholar.google.com/citations?user=TN8uDQoAAAAJ&hl=en&authuser=1), [Jie Zhou](https://scholar.google.com/citations?user=6a79aPwAAAAJ&hl=en&authuser=1)
This repository contains PyTorch implementation for GFNet (NeurIPS 2021 & T-PAMI).
Global Filter Networks is a transformer-style architecture that learns long-term spatial dependencies in the frequency domain with log-linear complexity. Our architecture replaces the self-attention layer in vision transformers with three key operations: a 2D discrete Fourier transform, an element-wise multiplication between frequency-domain features and learnable global filters, and a 2D inverse Fourier transform.
Our code is based on [pytorch-image-models](https://github.com/rwightman/pytorch-image-models) and [DeiT](https://github.com/facebookresearch/deit).
[[Project Page]](https://gfnet.ivg-research.xyz/) [[arXiv]](https://arxiv.org/abs/2107.00645)
## Global Filter Layer
GFNet is a conceptually simple yet computationally efficient architecture, which consists of several stacking Global Filter Layers and Feedforward Networks (FFN). The Global Filter Layer mixes tokens with log-linear complexity benefiting from the highly efficient Fast Fourier Transform (FFT) algorithm. The layer is easy to implement:
```python
import torch
import torch.nn as nn
import torch.fft
class GlobalFilter(nn.Module):
def __init__(self, dim, h=14, w=8):
super().__init__()
self.complex_weight = nn.Parameter(torch.randn(h, w, dim, 2, dtype=torch.float32) * 0.02)
def forward(self, x):
B, H, W, C = x.shape
x = torch.fft.rfft2(x, dim=(1, 2), norm='ortho')
weight = torch.view_as_complex(self.complex_weight)
x = x * weight
x = torch.fft.irfft2(x, s=(H, W), dim=(1, 2), norm='ortho')
return x
```
Compared to self-attention and spatial MLP, our Global Filter Layer is much more efficient to process high-resolution feature maps:
## Model Zoo
We provide our GFNet models pretrained on ImageNet:
| name | arch | Params | FLOPs | acc@1 | acc@5 | url |
| --- | --- | --- | --- | --- | --- | --- |
| GFNet-Ti | ```gfnet-ti``` | 7M | 1.3G | 74.6 | 92.2 | [Tsinghua Cloud](https://cloud.tsinghua.edu.cn/f/3d0c1579aa524a0a99dd/?dl=1) / [Google Drive](https://drive.google.com/file/d/1_xrfC7c_ccZnVicYDnrViOA_T1N-xoHI/view?usp=sharing)|
| GFNet-XS | ```gfnet-xs``` | 16M | 2.8G | 78.6 | 94.2 | [Tsinghua Cloud](https://cloud.tsinghua.edu.cn/f/e0ab5b1583954a1fa9b2/?dl=1) / [Google Drive](https://drive.google.com/file/d/1paf9gQWdsLXrG58R77yJ3U0FiNINg9xN/view?usp=sharing)|
| GFNet-S | ```gfnet-s``` | 25M | 4.5G | 80.0 | 94.9 | [Tsinghua Cloud](https://cloud.tsinghua.edu.cn/f/e5561fa070c44d9399bf/?dl=1) / [Google Drive](https://drive.google.com/file/d/18aRey_1abWNMmSL7TZQ4WxpplLRCDGEl/view?usp=sharing)|
| GFNet-B | ```gfnet-b``` | 43M | 7.9G | 80.7 | 95.1 | [Tsinghua Cloud](https://cloud.tsinghua.edu.cn/f/2fbf264597af4d72afb3/?dl=1) / [Google Drive](https://drive.google.com/file/d/1OncnXYAXpdjZBq4JK5Y3xacIHOIMePQo/view?usp=sharing)|
| GFNet-H-Ti | ```gfnet-h-ti``` | 15M | 2.0G | 80.1 | 95.1 | [Tsinghua Cloud](https://cloud.tsinghua.edu.cn/f/b22dd45eccbe462cbbfb/?dl=1) / [Google Drive](https://drive.google.com/file/d/1Nrq5sfHD9RklCMl6WkcVrAWI5vSVzwSm/view?usp=sharing)|
| GFNet-H-S | ```gfnet-h-s``` | 32M | 4.5G | 81.5 | 95.6 | [Tsinghua Cloud](https://cloud.tsinghua.edu.cn/f/5229cb4d1daf48e69675/?dl=1) / [Google Drive](https://drive.google.com/file/d/1w4d7o1LTBjmSkb5NKzgXBBiwdBOlwiie/view?usp=sharing)|
| GFNet-H-B | ```gfnet-h-b``` | 54M | 8.4G | 82.9 | 96.2 | [Tsinghua Cloud](https://cloud.tsinghua.edu.cn/f/954c5af21e824ba6b40c/?dl=1) / [Google Drive](https://drive.google.com/file/d/1F900_-yPH7GFYExcerpt of 6,550 characters
Read on GitHub
Yongming Rao · Tencent
12
Wenliang Zhao · Tsinghua University · China
1
Would you bet a product on this? Bounded 0–100 and slow moving.
matched fp:977260cc059f6092, topic:computer-vision, topic:image-classification, desc:image classification