SFM: Spatial Frequency Modulation for Semantic Segmentation (TPAMI 2025)

Official checkpoints for the paper Spatial Frequency Modulation for Semantic Segmentation (TPAMI 2025, arXiv:2507.11893, DOI 10.1109/TPAMI.2025.3592621).

Code: https://github.com/Linwei-Chen/SFM

Two released technical lines:

• Line 1 — FCN-style models: ARS + qhull non-uniform upsampling (MSAU, paper Eq. 17/18)
  • cascaded LPRM, built on mmsegmentation 0.25.0. Eval code and the 18 configs: SFM_mmseg0x/.
• Line 2 — FPN: ARS + CARAFE-style feature reassembly with 3-point dynamic-sampling weighted alignment, on Mask2Former (mmsegmentation 1.x). Code and config: SFM_mask2former/.

Every checkpoint below is verified from training/eval logs: each measured mIoU either exceeds the paper-reported number or matches it after one-decimal rounding (e.g. PSPNet-R50 D16 measures 78.05, rounding to the paper's 78.1). Each model folder contains config.py (the exact config paired with that checkpoint) and the weight file.

Line 1 — Cityscapes (val mIoU, ResNet-50)

folder	paper	measured
line1/cityscapes/ccnet_r50SS3PR-d32_cityscapes	76.3	76.34
line1/cityscapes/ccnet_r50SS3PR-d16_cityscapes	78.5	78.45
line1/cityscapes/ccnet_r50SS3PR-d8_cityscapes	79.3	79.39
line1/cityscapes/isanet_r50SS3PR-d32_cityscapes (OCNet)	75.8	75.82
line1/cityscapes/isanet_r50SS3PR-d16_cityscapes (OCNet)	78.0	78.00
line1/cityscapes/isanet_r50SS3PR-d8_cityscapes (OCNet)	79.7	79.74
line1/cityscapes/FCN_r50SS3PR-d32_cityscapes	76.2	76.20
line1/cityscapes/FCN_r50SS3PR-d16_cityscapes	77.8	77.78
line1/cityscapes/FCN_r50SS3PR-d8_cityscapes	78.7	78.65
line1/cityscapes/pspnet_r50SS3PR-d32_cityscapes	76.0	76.18
line1/cityscapes/pspnet_r50SS3PR-d16_cityscapes	78.1	78.05
line1/cityscapes/pspnet_r50SS3PR-d8_cityscapes	79.5	79.52
line1/cityscapes/pcaa_r50SSPR-d32_cityscapes (PCAA, col. 1)	76.8	76.83
line1/cityscapes/pcaa_r50SSPR-d16_cityscapes (PCAA, col. 2)	77.9	77.86

Weight file in each folder: iter_40000.pth.

Line 1 — PascalContext-59 (val mIoU, ResNet-50)

folder	paper	measured
line1/pascalcontext/FCN_r50SS3PR-d32_pascalcontext	46.0	46.35
line1/pascalcontext/pspnet_r50SS3PR-d32_pascalcontext	49.7	49.68
line1/pascalcontext/ccnet_r50SS3PR-d32_pascalcontext	49.1	49.07
line1/pascalcontext/isanet_r50SS3PR-d32_pascalcontext (OCNet)	49.9	49.85

Line 2 — ADE20K (val mIoU, Mask2Former Swin-T)

folder	paper	measured
line2/mask2former_swin-t_8xb2-160k_ade20k-512x512_SFMv2	49.1	49.34 (re-evaluated 2025-10)

Weight file: best_mIoU_iter_150000.pth.

Evaluation

Line 1 (see SFM_mmseg0x/README.md for environment setup and qhull compilation):

cd SFM_mmseg0x    # from the GitHub repo checkout
PYTHONPATH=<SFM_mmseg0x> python tools/test.py \
    <model_folder>/config.py <model_folder>/iter_40000.pth --eval mIoU

Line 2 (code: SFM_mask2former/, mmsegmentation 1.x):

cd SFM_mask2former
python tools/test.py \
    <model_folder>/config.py <model_folder>/best_mIoU_iter_150000.pth

Citation

@article{chen2023spatialfrequency,
  title={Spatial Frequency Modulation for Semantic Segmentation},
  author={Chen, Linwei and Fu, Ying and Gu, Lin and Zheng, Dezhi and Dai, Jifeng},
  journal={IEEE Transactions on Pattern Analysis and Machine Intelligence},
  year={2025},
  volume={47},
  number={11},
  pages={9767-9784},
  doi={10.1109/TPAMI.2025.3592621}
}

Code is released under the Apache-2.0 license.