SPANet Official

💬 This repo is the official implementation of:

• ICCV2023: SPANet: Frequency-balancing Token Mixer using Spectral Pooling Aggregation Modulation

🗞️ News:

• arXiv2025: Spectral-Adaptive Modulation Networks for Visual Perception

🤖 It currently includes code and models for the following tasks:

• Image Classification
• Object Detection
• [] Semantic Segmentation

📖 Introduction

SPANet is a new backbone network which can handle the balance problem of high- and low-frequency components for optimal feature representations.

Main results on ImageNet-1K

Please see image_classification for more details.

Model	Pretrain	Resolution	Top-1	#Param.	FLOPs
SPANet-S	ImageNet-1K	224x224	83.1	28.7M	4.6G
SPANet-M	ImageNet-1K	224x224	83.5	41.8M	6.8G
SPANet-MX	ImageNet-1K	224x224	83.8	54.9M	9.0G
SPANet-B	ImageNet-1K	224x224	84.0	75.9M	12.0G
SPANet-BX	ImageNet-1K	224x224	84.4	99.8 M	15.8G

Main results on COCO object detection and instance segmentation

Please see object_detection for more details.

RetinaNet 1x

Backbone	Lr Schd	box mAP	#params
SPANet-S	1x	43.3	38M
SPANet-M	1x	44.0	51M

Mask R-CNN 1x

Backbone	Lr Schd	box mAP	mask mAP	#params
SPANet-S	1x	44.7	40.6	48M
SPANet-M	1x	45.2	41.0	61M

Main results on ADE20K semantice segmentation

Please see semantic_segmentation for more details.

Semantic FPN

Backbone	Lr Schd	mIoU	#params	FLOPs
SPANet-S	80K	45.4	32M	46G
SPANet-M	80K	46.2	45M	57G

⭐ Cite SPANet

If you find this repository useful, please give us stars and use the following BibTeX entry for citation.

@inproceedings{yun2023spanet,
  title={SPANet: Frequency-balancing Token Mixer using Spectral Pooling Aggregation Modulation},
  author={Yun, Guhnoo and Yoo, Juhan and Kim, Kijung and Lee, Jeongho and Kim, Dong Hwan},
  booktitle={Proceedings of the IEEE/CVF International Conference on Computer Vision},
  pages={6113--6124},
  year={2023}
}

License

This project is released under the MIT license. Please see the LICENSE file for more information.